{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8798b8c1",
   "metadata": {},
   "source": [
    "##  手动实现前馈神经网络解决回归任务"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "51e3e7f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "当前使用的device为cuda\n"
     ]
    }
   ],
   "source": [
    "# 导入模块\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import numpy as np\n",
    "import torchvision\n",
    "from torchvision import transforms\n",
    "import time\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\") # 如果有gpu则在gpu上计算 加快计算速度\n",
    "print(f'当前使用的device为{device}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cc9ea892",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义绘图函数\n",
    "import matplotlib.pyplot as plt\n",
    "def draw_loss(train_loss, test_loss):\n",
    "    x = np.linspace(0, len(train_loss), len(train_loss))\n",
    "    plt.plot(x, train_loss, label=\"Train Loss\", linewidth=1.5)\n",
    "    plt.plot(x, test_loss, label=\"Test Loss\", linewidth=1.5)\n",
    "    plt.xlabel(\"Epoch\")\n",
    "    plt.ylabel(\"Loss\")\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "41e7dfe4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_acc(train_acc, test_acc):\n",
    "    x = np.linspace(0, len(train_acc), len(train_acc))\n",
    "    plt.plot(x, train_acc, label=\"Train Acc\", linewidth=1.5)\n",
    "    plt.plot(x, test_acc, label=\"Test Acc\", linewidth=1.5)\n",
    "    plt.xlabel(\"Epoch\")\n",
    "    plt.ylabel(\"Acc\")\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9efb0d15",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义评价函数\n",
    "def evaluate_accuracy(data_iter, model, loss_func):\n",
    "    acc_sum, test_l_sum, n, c = 0.0, 0.0, 0, 0\n",
    "    for X, y in data_iter:\n",
    "        result = model.forward(X)\n",
    "        acc_sum += (result.argmax(dim=1)==y).float().sum().item()\n",
    "        test_l_sum += loss_func(result, y).item()\n",
    "        n += y.shape[0]\n",
    "        c += 1\n",
    "    return acc_sum/n, test_l_sum/c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "838f7538",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 回归任务\n",
    "n_train = 7000\n",
    "n_test = 3000\n",
    "num_inputs = 500\n",
    "true_w, true_b = torch.ones(num_inputs, 1)*0.0056, 0.028\n",
    "\n",
    "# 生成数据集\n",
    "features = torch.randn((n_train + n_test, num_inputs))\n",
    "labels = torch.matmul(features, true_w) + true_b\n",
    "labels += torch.tensor(np.random.normal(0, 0.01, size=labels.size()), dtype=torch.float)\n",
    "# 数据划分\n",
    "train_features, test_features = features[:n_train, :], features[n_train:, :]\n",
    "train_labels, test_labels = labels[:n_train], labels[n_train:]\n",
    "batch_size1 = 128\n",
    "\n",
    "traindataset1 = torch.utils.data.TensorDataset(train_features, train_labels)\n",
    "testdataset1 = torch.utils.data.TensorDataset(test_features, test_labels)\n",
    "\n",
    "traindataloader1 = torch.utils.data.DataLoader(dataset=traindataset1, batch_size=batch_size1, shuffle=True)\n",
    "testdataloader1 = torch.utils.data.DataLoader(dataset=testdataset1, batch_size=batch_size1, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "42c85a4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义损失函数\n",
    "def my_cross_entropy_loss(y_hat, labels):\n",
    "    def log_softmax(y_hat):\n",
    "        max_v = torch.max(y_hat, dim=1).values.unsqueeze(dim=1)\n",
    "        return y_hat - max_v - torch.log(torch.exp(y_hat-max_v).sum(dim=1).unsqueeze(dim=1))\n",
    "\n",
    "    return (-log_softmax(y_hat))[range(len(y_hat)), labels].mean()\n",
    "\n",
    "# 定义优化算法\n",
    "def SGD(params, lr):\n",
    "    for param in params:\n",
    "        param.data -= lr*param.grad\n",
    "\n",
    "def mse(pred, true):\n",
    "    ans = torch.sum((true-pred)**2)/len(pred)\n",
    "    return ans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9c687863",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Net1():\n",
    "    def __init__(self):\n",
    "        # 设置隐藏层和输出层的节点数\n",
    "        num_inputs, num_hiddens, num_outputs = 500, 256, 1\n",
    "        w_1 = torch.tensor(np.random.normal(0,0.01,(num_hiddens,num_inputs)),dtype=torch.float32,requires_grad=True)\n",
    "        b_1 = torch.zeros(num_hiddens, dtype=torch.float32,requires_grad=True)\n",
    "        w_2 = torch.tensor(np.random.normal(0, 0.01,(num_outputs, num_hiddens)),dtype=torch.float32,requires_grad=True)\n",
    "        b_2 = torch.zeros(num_outputs,dtype=torch.float32, requires_grad=True)\n",
    "        self.params = [w_1, b_1, w_2, b_2]\n",
    "\n",
    "        # 定义模型结构\n",
    "        self.input_layer = lambda x: x.view(x.shape[0],-1)\n",
    "        self.hidden_layer = lambda x: self.my_relu(torch.matmul(x,w_1.t())+b_1)\n",
    "        self.output_layer = lambda x: torch.matmul(x,w_2.t()) + b_2\n",
    "\n",
    "    def my_relu(self, x):\n",
    "        return torch.max(input=x,other=torch.tensor(0.0))\n",
    "\n",
    "    def forward(self, x):\n",
    "        flatten_input = self.input_layer(x)\n",
    "        hidden_output = self.hidden_layer(flatten_input)\n",
    "        final_output = self.output_layer(hidden_output)\n",
    "        return final_output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "888aa8f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 1 | train loss:0.88 | test loss:0.85\n",
      "epoch: 2 | train loss:0.84 | test loss:0.82\n",
      "epoch: 3 | train loss:0.81 | test loss:0.80\n",
      "epoch: 4 | train loss:0.79 | test loss:0.77\n",
      "epoch: 5 | train loss:0.76 | test loss:0.75\n",
      "epoch: 6 | train loss:0.74 | test loss:0.72\n",
      "epoch: 7 | train loss:0.71 | test loss:0.70\n",
      "epoch: 8 | train loss:0.69 | test loss:0.67\n",
      "epoch: 9 | train loss:0.66 | test loss:0.65\n",
      "epoch: 10 | train loss:0.63 | test loss:0.62\n",
      "epoch: 11 | train loss:0.61 | test loss:0.59\n",
      "epoch: 12 | train loss:0.58 | test loss:0.56\n",
      "epoch: 13 | train loss:0.55 | test loss:0.53\n",
      "epoch: 14 | train loss:0.52 | test loss:0.50\n",
      "epoch: 15 | train loss:0.49 | test loss:0.47\n",
      "epoch: 16 | train loss:0.46 | test loss:0.44\n",
      "epoch: 17 | train loss:0.43 | test loss:0.41\n",
      "epoch: 18 | train loss:0.40 | test loss:0.38\n",
      "epoch: 19 | train loss:0.37 | test loss:0.35\n",
      "epoch: 20 | train loss:0.34 | test loss:0.33\n",
      "手动实现前馈网络-回归实验 20轮 总用时: 4.76s\n"
     ]
    }
   ],
   "source": [
    "# 训练\n",
    "model1 = Net1()  # logistics模型\n",
    "criterion = my_cross_entropy_loss\n",
    "lr = 0.01   # 学习率\n",
    "batchsize = 128 \n",
    "epochs = 20 #训练轮数\n",
    "train_all_loss1 = [] # 记录训练集上得loss变化\n",
    "test_all_loss1 = [] #记录测试集上的loss变化\n",
    "begintime1 = time.time()\n",
    "for epoch in range(epochs):\n",
    "    train_l = 0\n",
    "    for data, labels in traindataloader1:\n",
    "        pred = model1.forward(data)\n",
    "        \n",
    "        train_each_loss = mse(pred.view(-1,1), labels.view(-1,1)) #计算每次的损失值\n",
    "        train_each_loss.backward() # 反向传播\n",
    "        SGD(model1.params, lr) # 使用小批量随机梯度下降迭代模型参数\n",
    "        # 梯度清零\n",
    "        train_l += train_each_loss.item()\n",
    "        for param in model1.params:\n",
    "            param.grad.data.zero_()\n",
    "        # print(train_each_loss)\n",
    "    train_all_loss1.append(train_l) # 添加损失值到列表中\n",
    "    with torch.no_grad():\n",
    "        test_loss = 0\n",
    "        for data, labels in traindataloader1:\n",
    "            pred = model1.forward(data)\n",
    "            test_each_loss = mse(pred, labels)\n",
    "            test_loss += test_each_loss.item()\n",
    "        test_all_loss1.append(test_loss)\n",
    "        print('epoch: %d | train loss:%.2f | test loss:%.2f'%(epoch+1,train_all_loss1[-1],test_all_loss1[-1]))\n",
    "endtime1 = time.time()\n",
    "print(\"手动实现前馈网络-回归实验 %d轮 总用时: %.2fs\"%(epochs,endtime1-begintime1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "23346b28",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_loss(train_all_loss1,test_all_loss1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84cdfcf2",
   "metadata": {},
   "source": [
    "## 手动实现前馈神经网络二分类任务"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0118b645",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 二分类任务\n",
    "data_num2, train_num2, test_num2 =10000, 7000, 3000\n",
    "# 第一个数据集 符合均值为 0.5 标准差为1 得分布\n",
    "featuresA = torch.normal(mean=0.2, std=4, size=(data_num2, 200), dtype=torch.float32)\n",
    "labelsA = torch.ones(data_num2)\n",
    "# 第二个数据集 符合均值为 -0.5 标准差为1的分布\n",
    "featuresB = torch.normal(mean=-0.2, std=4, size=(data_num2, 200), dtype=torch.float32)\n",
    "labelsB = torch.zeros(data_num2)\n",
    "\n",
    "# 构建训练数据集\n",
    "train_features2 = torch.cat((featuresA[:train_num2], featuresB[:train_num2]), dim=0) \n",
    "train_labels2 = torch.cat((labelsA[:train_num2], labelsB[:train_num2]), dim=-1) \n",
    "# 构建测试数据集\n",
    "test_features2 = torch.cat((featuresA[train_num2:], featuresB[train_num2:]), dim=0)  \n",
    "test_labels2 = torch.cat((labelsA[train_num2:], labelsB[train_num2:]), dim=-1) \n",
    "batch_size = 128\n",
    "# Build the training and testing dataset\n",
    "traindataset2 = torch.utils.data.TensorDataset(train_features2, train_labels2)\n",
    "testdataset2 = torch.utils.data.TensorDataset(test_features2, test_labels2)\n",
    "traindataloader2 = torch.utils.data.DataLoader(dataset=traindataset2,batch_size=batch_size,shuffle=True)\n",
    "testdataloader2 = torch.utils.data.DataLoader(dataset=testdataset2,batch_size=batch_size,shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ac241d2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.nn.functional import binary_cross_entropy\n",
    "from torch.nn import CrossEntropyLoss\n",
    "class Net2():\n",
    "    def __init__(self):\n",
    "        # 设置隐藏层和输出层的节点数\n",
    "        num_inputs, num_hiddens, num_outputs = 200, 256, 1\n",
    "        w_1 = torch.tensor(np.random.normal(0, 0.01, (num_hiddens, num_inputs)), dtype=torch.float32,\n",
    "                           requires_grad=True)\n",
    "        b_1 = torch.zeros(num_hiddens, dtype=torch.float32, requires_grad=True)\n",
    "        w_2 = torch.tensor(np.random.normal(0, 0.01, (num_outputs, num_hiddens)), dtype=torch.float32,\n",
    "                           requires_grad=True)\n",
    "        b_2 = torch.zeros(num_outputs, dtype=torch.float32, requires_grad=True)\n",
    "        self.params = [w_1, b_1, w_2, b_2]\n",
    "\n",
    "        # 定义模型结构\n",
    "        self.input_layer = lambda x: x.view(x.shape[0], -1)\n",
    "        self.hidden_layer = lambda x: self.my_relu(torch.matmul(x, w_1.t()) + b_1)\n",
    "        self.output_layer = lambda x: torch.matmul(x, w_2.t()) + b_2\n",
    "        self.fn_logistic = self.logistic\n",
    "\n",
    "    def my_relu(self, x):\n",
    "        return torch.max(input=x, other=torch.tensor(0.0))\n",
    "\n",
    "    def logistic(self, x):  # 定义logistic函数\n",
    "        x = 1.0 / (1.0 + torch.exp(-x))\n",
    "        return x\n",
    "\n",
    "    # 定义前向传播\n",
    "    def forward(self, x):\n",
    "        x = self.input_layer(x)\n",
    "        x = self.my_relu(self.hidden_layer(x))\n",
    "        x = self.fn_logistic(self.output_layer(x))\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2d7d46f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 1 | train loss:74.59 | test loss:30.94 | train acc:0.639 | test acc:0.719\n",
      "epoch: 2 | train loss:68.69 | test loss:27.72 | train acc:0.742 | test acc:0.743\n",
      "epoch: 3 | train loss:60.28 | test loss:24.84 | train acc:0.764 | test acc:0.745\n",
      "epoch: 4 | train loss:54.80 | test loss:23.78 | train acc:0.769 | test acc:0.748\n",
      "epoch: 5 | train loss:52.69 | test loss:23.54 | train acc:0.774 | test acc:0.750\n",
      "epoch: 6 | train loss:51.65 | test loss:23.54 | train acc:0.778 | test acc:0.751\n",
      "epoch: 7 | train loss:50.88 | test loss:23.58 | train acc:0.783 | test acc:0.751\n",
      "epoch: 8 | train loss:50.27 | test loss:23.62 | train acc:0.786 | test acc:0.752\n",
      "epoch: 9 | train loss:49.63 | test loss:23.66 | train acc:0.790 | test acc:0.752\n",
      "epoch: 10 | train loss:48.98 | test loss:23.69 | train acc:0.793 | test acc:0.752\n",
      "epoch: 11 | train loss:48.33 | test loss:23.76 | train acc:0.797 | test acc:0.751\n",
      "epoch: 12 | train loss:47.62 | test loss:23.78 | train acc:0.803 | test acc:0.750\n",
      "epoch: 13 | train loss:46.90 | test loss:23.87 | train acc:0.807 | test acc:0.749\n",
      "epoch: 14 | train loss:46.23 | test loss:23.93 | train acc:0.808 | test acc:0.748\n",
      "epoch: 15 | train loss:45.51 | test loss:23.96 | train acc:0.816 | test acc:0.748\n",
      "epoch: 16 | train loss:44.67 | test loss:24.02 | train acc:0.821 | test acc:0.746\n",
      "epoch: 17 | train loss:43.74 | test loss:24.09 | train acc:0.825 | test acc:0.747\n",
      "epoch: 18 | train loss:42.84 | test loss:24.22 | train acc:0.832 | test acc:0.745\n",
      "epoch: 19 | train loss:42.01 | test loss:24.32 | train acc:0.838 | test acc:0.744\n",
      "epoch: 20 | train loss:40.95 | test loss:24.33 | train acc:0.848 | test acc:0.747\n",
      "手动实现前馈网络-二分类实验 20轮 总用时: 8.78s\n"
     ]
    }
   ],
   "source": [
    "# 训练\n",
    "model2 = Net2()\n",
    "lr = 0.01  # 学习率\n",
    "epochs = 20  # 训练轮数\n",
    "train_all_loss2 = []  # 记录训练集上得loss变化\n",
    "test_all_loss2 = []  # 记录测试集上的loss变化\n",
    "train_Acc12, test_Acc12 = [], []\n",
    "begintime2 = time.time()\n",
    "for epoch in range(epochs):\n",
    "    train_l, train_epoch_count = 0, 0\n",
    "    for data, labels in traindataloader2:\n",
    "        pred = model2.forward(data)\n",
    "        train_each_loss = binary_cross_entropy(pred.view(-1), labels.view(-1))  # 计算每次的损失值\n",
    "        train_l += train_each_loss.item()\n",
    "        train_each_loss.backward()  # 反向传播\n",
    "        SGD(model2.params, lr)  # 使用随机梯度下降迭代模型参数\n",
    "        # 梯度清零\n",
    "        for param in model2.params:\n",
    "            param.grad.data.zero_()\n",
    "        # print(train_each_loss)\n",
    "        train_epoch_count += (torch.tensor(np.where(pred > 0.5, 1, 0)).view(-1) == labels).sum()\n",
    "    train_Acc12.append((train_epoch_count/len(traindataset2)).item())\n",
    "    train_all_loss2.append(train_l)  # 添加损失值到列表中\n",
    "    with torch.no_grad():\n",
    "        test_l, test_epoch_count = 0, 0\n",
    "        for data, labels in testdataloader2:\n",
    "            pred = model2.forward(data)\n",
    "            test_each_loss = binary_cross_entropy(pred.view(-1), labels.view(-1))\n",
    "            test_l += test_each_loss.item()\n",
    "            test_epoch_count += (torch.tensor(np.where(pred > 0.5, 1, 0)).view(-1) == labels.view(-1)).sum()\n",
    "        test_Acc12.append((test_epoch_count/len(testdataset2)).item())\n",
    "        test_all_loss2.append(test_l)\n",
    "        print('epoch: %d | train loss:%.2f | test loss:%.2f | train acc:%.3f | test acc:%.3f'  % (epoch + 1, train_all_loss2[-1], test_all_loss2[-1], train_Acc12[-1], test_Acc12[-1]))\n",
    "endtime2 = time.time()\n",
    "print(\"手动实现前馈网络-二分类实验 %d轮 总用时: %.2fs\" % (epochs, endtime2 - begintime2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "72fc30d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9v0ehdJJh8/ND0dVfLXZJRER2qbS0FGlpaejUqRNcXFzELodaSEP7tbHHb1FHLfn5+dlcHOjnn39Gly5d8Ic//AF6vR7Lli3DokWLMGzYMPTr1w/Lly/Hvn37cODAgXrXWVZWBoPBYPOwV/d1D8DwKH9UGAW8sT4ZImZKIiIiSbKb4dfl5eX45ptv8Pjjj0Mmk+Ho0aOoqKhAQkKCdZmoqCiEhoZi//799a5n/vz50Gq11kdISEhblN8sMpkMc8f0gItSjgOX8vDjCZF64BMREUmU3QSZ9evXIz8/H9OmTQMA6HQ6ODs7Wzs2VQkICIBOp6t3Pa+99hr0er31UXUVQnsV4u2GWcMiAADvbDwDfUmFyBURERFJh90EmWXLlmHkyJEIDr6zKzWqVCpoNBqbh717amhndPFzx/XCcnz4S6rY5RAREUmGXQSZ9PR0bN++HU8++aR1WmBgIMrLy5Gfn2+zbE5Ozh1dktkeOSvkeHtsTwDAfw+k49SVfHELIiKyU+xL6FhaYn/aRZBZvnw5/P39MWrUKOu0fv36QalUYseOHdZpqampyMjIQHx8vBhltqrBXX0xtncwBAF4Y30yjCZ+WYmIqlRdLba4mLd2cSRV+/NOrgYs+gXxTCYTli9fjqlTp0KhuFWOVqvFE088gZdeegne3t7QaDSYNWsW4uPjMWjQIBErbj2vj4rGr2dzceqKHisPpuOR+HCxSyIisgtOTk7w9PREbm4uAMDNzQ2yO7wDNYlHEAQUFxcjNzcXnp6ezb6DN2AHQWb79u3IyMjA448/XmveRx99BLlcjokTJ6KsrAyJiYlYvHixCFW2DX+1C15JjMScDaexYGsqRvQMgp9aJXZZRER2oapbQVWYIem70zt4A4CoF8RrC/Z8Qby6GE0Cxn72O5KvGjChTwcsmtxb7JKIiOyK0WhERQVHeEqdUqlssCWmscdv0VtkyJaTXIZ3x8Vg3OK9WHv8Kh4b0gkxHbVil0VEZDecnJzu6FQEORa76OxLtmJDPDE21jwMffm+NJGrISIisl8MMnbqsSGdAAA/n8zGtYIykashIiKyTwwydio2xBN9Qj1RbjRh1aEMscshIiKySwwydmza4HAAwDcH0lFeaRK3GCIiIjvEIGPHRlqGX+cWlGHL6frvL0VERNReMcjYMWeFHH+JCwMArNjLTr9EREQ1McjYuT/HhULpJMOxjHzeg4mIiKgGBhk756dW4Y+9zEOxV+y7LG4xREREdoZBRgKqOv1yKDYREZEtBhkJ4FBsIiKiujHISASHYhMREdXGICMRHIpNRERUG4OMRHAoNhERUW0MMhLCodhERES2GGQkhEOxiYiIbDHISAyHYhMREd3CICMxHIpNRER0C4OMBHEoNhERkRmDjARxKDYREZEZg4wEcSg2ERGRGYOMRD0cF8Kh2ERE1O4xyEiUv9qFQ7GJiKjdY5CRMA7FJiKi9o5BRsJiQzzRO4RDsYmIqP1ikJG4x4aEAzAPxa4wcig2ERG1LwwyEld9KPbmZA7FJiKi9oVBRuI4FJuIiNozBhkHwKHYRETUXjHIOAAOxSYiovaKQcZBcCg2ERG1RwwyDoJDsYmIqD1ikHEgHIpNRETtDYOMA+FQbCIiam8YZByIs0KOKXGhADgUm4iI2gcGGQfz57hQDsUmIqJ2g0HGwXAoNhERtScMMg5oKodiExFRO8Eg44B6cyg2ERG1EwwyDopDsYmIqD1gkHFQHIpNRETtAYOMg+JQbCIiag8YZBwYh2ITEZGjY5BxYByKTUREjo5BxsFxKDYRETkyBhkHx6HYRETkyBhk2gEOxSYiIkfFINMOcCg2ERE5KgaZdoBDsYmIyFExyLQTHIpNRESOiEGmnfBXu2BUTBAADsUmIiLHwSDTjkwb0gkAh2ITEZHjYJBpRzgUm4iIHA2DTDvDodhERORIRA8yV69exV/+8hf4+PjA1dUVMTExOHLkiHW+IAiYPXs2goKC4OrqioSEBJw/f17EiqWNQ7GJiMiRiBpkbt68iSFDhkCpVGLz5s04c+YMPvzwQ3h5eVmXWbBgAT755BMsXboUBw8ehLu7OxITE1FaWipi5dJVfSj2st/TIAiCyBURERE1n0wQ8Uj297//HXv37sWePXvqnC8IAoKDg/Hyyy/jlVdeAQDo9XoEBARgxYoVeOihh277GQaDAVqtFnq9HhqNpkXrl6rcglLcvWAnSitMeG98DP5sCTZERET2orHHb1FbZDZs2ID+/fvjT3/6E/z9/dGnTx98+eWX1vlpaWnQ6XRISEiwTtNqtYiLi8P+/fvrXGdZWRkMBoPNg2z5q13w18QoAMB7m87ian6JyBURERE1j6hB5tKlS1iyZAkiIiKwdetWzJgxA8899xz+/e9/AwB0OnMfjoCAAJv3BQQEWOfVNH/+fGi1WusjJCSkdTdCoqYNDkffUE8UllXi/9Ym8RQTERFJkqhBxmQyoW/fvnjvvffQp08fTJ8+HU899RSWLl3a7HW+9tpr0Ov11kdmZmYLVuw4nOQyLJgUC2eFHLvPXcP3R6+IXRIREVGTiRpkgoKC0L17d5tp0dHRyMgwX+MkMDAQAJCTk2OzTE5OjnVeTSqVChqNxuZBdevq74GX7usGAHj75zPIMbADNRERSYuoQWbIkCFITU21mXbu3DmEhYUBADp16oTAwEDs2LHDOt9gMODgwYOIj49v01od1ZN3dUJsRy0MpZV4fR1PMRERkbSIGmRefPFFHDhwAO+99x4uXLiAlStX4osvvsDMmTMBADKZDC+88ALeeecdbNiwAUlJSXj00UcRHByMcePGiVm6w1A4ybFgUiyUTjJsP5uLDSezxC6JiIio0UQNMgMGDMC6deuwatUq9OzZE2+//TY+/vhjTJkyxbrM3/72N8yaNQvTp0/HgAEDUFhYiC1btsDFxUXEyh1LZKAas4ZFAADmbDjN+zAREZFkiHodmbbA68g0ToXRhLH/2osz2QY8EBOIxVP6iV0SERG1Y5K4jgzZD6WTHAsm9YJCLsOmJB02JWWLXRIREdFtMciQVc8OWsy4pwsAYPaPycgrKhe5IiIiooYxyJCNZ4d1RbcAD1wvLMe8n06LXQ4REVGDGGTIhkrhhIWTYiGXAT+eyMK2Mzm3fxMREZFIGGSoltgQTzx1d2cAwOvrkqAvrhC5IiIioroxyFCdXkzohs6+7sgtKMM7G8+IXQ4REVGdGGSoTi5KJyyY1AsyGbDm6BXsSs0VuyQiIqJaGGSoXv3DvTFtcDgA4LW1SSgo5SkmIiKyLwwy1KC/JkYi1NsN2fpSzN+cInY5RERENhhkqEFuzgp8MLEXAGDlwQzsu3Bd5IqIiIhuYZCh24rv4oO/DAoFALy69hSKyipFroiIiMiMQYYa5e8jo9HB0xWZeSVYuDVV7HKIiIgAMMhQI3moFJg/IQYAsGLfZRxKyxO5IiIiIgYZaoK7u/lhcv8QAMCrP5xCSblR5IqIiKi9Y5ChJvm/UdEI0KiQdr0Ii7bxFBMREYmLQYaaROuqxHvjzaeYlv2ehmMZN0WuiIiI2jMGGWqy4dEBGN+nA0wC8LfvT6G0gqeYiIhIHAwy1CxzRneHr4cKF3IL8emv58Uuh4iI2ikGGWoWTzdnvDOuJwBg6e5LSLqiF7kiIiJqjxhkqNlG9AzEqF5BMJoE/PX7kyivNIldEhERtTMMMnRH3hrTA97uzkjRFWDxrgtil0NERO0MgwzdER8PFeaO6QEA+NevF3A22yByRURE1J4wyNAdG90rCPd3D0Cl5RRTpZGnmIiIqG0wyNAdk8lkeGdcT2hdlUi+asDnv10SuyQiImonGGSoRfhrXDD7j90BAP/cfh4XcgtEroiIiNoDBhlqMRP6dsC9kX4oN5rw1+9PwWgSxC6JiIgcHIMMtRiZTIb3JsRArVLgeEY+3vwxGYVllWKXRUREDoxBhlpUkNYVb442n2JaeTAD9/5jF9YeuwITW2eIiKgVMMhQi3uwfwi+ntYf4T5uuFZQhpe+O4lJS/fh1JV8sUsjIiIHIxMEwaH/VDYYDNBqtdDr9dBoNGKX066UVRrx9e+X8emv51FcboRMBkzuH4JXEiPh66ESuzwiIrJjjT1+M8hQq8sxlOL9zSlYd/wqAEDtosALCd3waHwYlE5sFCQiotoYZCwYZOzHkct5mPvTaSRfNV/9t6u/B+aO7oG7InxFroyIiOwNg4wFg4x9MZoEfHckEwu3piKvqBwAkNgjAG+M6o4QbzeRqyMiInvBIGPBIGOf9MUV+HjHOfxnfzqMJgHOCjmevrszZtzTFa7OTmKXR0REImOQsWCQsW/ncgowd8Np7Lt4AwAQrHXB/42KxqiYIMhkMpGrIyIisTDIWDDI2D9BELD1tA5v/3wWV/NLAABxnbwxd0wPRAdxnxERtUcMMhYMMtJRWmHE57svYfGuCyirNEEuA/4yKAwv3dcNnm7OYpdHRERtiEHGgkFGeq7cLMb8TSnYmJQNAPB0U+KV+yPx8MBQOMl5uomIqD1gkLFgkJGufRevY96GM0jNMd9JOzpIg3ljemBgJ2+RKyMiotbGIGPBICNtlUYTVh7KwIe/nIO+pAIAMCY2GK89EIUgravI1RERUWthkLFgkHEMeUXl+PCXVKw8lAFBAFQKOYZG+OGeSPOjoxevQUNE5EgYZCwYZBxL8lU95v10Gocv37SZ3i3AA/dE+uOeSD/0D/OGs4K3PiAikjIGGQsGGccjCAJOZxmw+9w17ErNxdH0mzBV+y32UCkwpKsP7o30xz2R/gjUuohXLBERNQuDjAWDjOPTF1dgz4Vr2JlyDbvP5eJ6YbnN/KhANe6J9Me9kX7oG+bFG1USEUkAg4wFg0z7YjKZW2t2puZiV2oujmfmo/pvuNpFgaERvubTUN384K9haw0RkT1ikLFgkGnf8orKsef8NexKvYbd565Zb1RZpUewxnIKyg+9QzyhYGsNEZFdYJCxYJChKkaTgKSreuxMMbfWnLqqt2mt0boqMTTCF/dG+mNoN1/4q9laQ0QkFgYZCwYZqs/1wjL8du5Wa03VdWqqdPB0Re8QT/QO8URsiCdiOmh5Z24iojbCIGPBIEONYTQJOJGZj12pudiZmovTWQbU/GY4yWXoFqBG7xCtNdxE+Kt52wQiolbAIGPBIEPNUVhWiVNX8nEyU48TmTdxMlMPnaG01nJuzk6I6aBF71BP9O7oid6hngjUuEAmY7ghIroTDDIWDDLUUnT6UpzIzMeJzHyczMzHqSv5KCo31lrOX62yttj0CfFETEct1C5KESomIpIuBhkLBhlqLUaTgIvXCnEiIx8nruTjREY+UnMKYDTZfqVkMqCrnwdiLf1teod4oluAmlcfJiJqAIOMBYMMtaWSciOSs/Q4mZmP45aWmys3S2otp5DL0MnXHd0C1JaHB7oFqhHm7cYh4EREYJCxYpAhsV0rKMOpK/k2p6UMpZV1LuuskKOLn4c52FhCTmSAGh29XCFnp2IiakdaNchkZmZCJpOhY8eOAIBDhw5h5cqV6N69O6ZPn978qlsBgwzZG0EQkK0vxbmcApzLKUCqrhDncwtwPqcQJRW1+9wAgKvSCREBHojwVyMy0AMRloATpGXHYiJyTK0aZIYOHYrp06fjkUcegU6nQ2RkJHr06IHz589j1qxZmD17dqPWM3fuXMybN89mWmRkJFJSUgAApaWlePnll7F69WqUlZUhMTERixcvRkBAQKNrZZAhqTCZBFy5WYJUS8AxPwpxMbcQ5UZTne9RqxToGuCByAC1Ndx0C/SAn4eKAYeIJK2xx29Fc1aenJyMgQMHAgC+++479OzZE3v37sUvv/yCp59+utFBBgB69OiB7du33ypIcaukF198ERs3bsSaNWug1Wrx7LPPYsKECdi7d29zyiaya3K5DKE+bgj1ccN93W+F9UqjCel5xThvab05l1uAc7oCpF0vQkFZJY5n5ON4Rr7NurzdnREZoEZUkBpRgWpEBWrQLUDNC/oRkcNpVpCpqKiASqUCAGzfvh1jxowBAERFRSE7O7tpBSgUCAwMrDVdr9dj2bJlWLlyJYYNGwYAWL58OaKjo3HgwAEMGjSoOaUTSY7CydxvpoufB0b0vDW9vNKEtOtF1VpvzC046TeKkFdUjv2XbmD/pRvW5WUyINzH3RpsIgPViA5SI8TLjf1viEiymhVkevTogaVLl2LUqFHYtm0b3n77bQBAVlYWfHx8mrSu8+fPIzg4GC4uLoiPj8f8+fMRGhqKo0ePoqKiAgkJCdZlo6KiEBoaiv3799cbZMrKylBWVmZ9bTAYmrGFRPbPWSFHZKAakYFqm+mlFUaczynEWZ0BqboCpOgMSMkuwI2icqRdL0La9SJsTtZZl3dzdkK3AHOoMbfiaBAVqIanm3NbbxIRUZM1K8h88MEHGD9+PBYuXIipU6ciNjYWALBhwwbrKafGiIuLw4oVKxAZGYns7GzMmzcPQ4cORXJyMnQ6HZydneHp6WnznoCAAOh0urpXCGD+/Pm1+t0QtScuSifEdNQipqPWZvq1gjKkWMLN2WxzwDmfW4jicqN1RFV1gRoXRAWZg1J0oAZRQWp09vXg9W+IyK40e/i10WiEwWCAl5eXddrly5fh5uYGf3//ZhWTn5+PsLAwLFq0CK6urnjsscdsWlcAYODAgbj33nvxwQcf1LmOulpkQkJC2NmXqA6VRhMu3yhCiq4AKZZwk6IrqPPaNwCgdJKhi5+H+fSUpeUmOkgDfzU7FxNRy2rVzr4lJSUQBMEaYtLT07Fu3TpER0cjMTGxeRUD8PT0RLdu3XDhwgXcd999KC8vR35+vk2rTE5OTp19aqqoVCpr/x0iapjCSY6u/mp09Vfjj71uTS8orcC5nFstN6mWoFNQVmkOPboC4ESWdXkvNyWiLK02Va033QLUcFGyczERta5mBZmxY8diwoQJePrpp5Gfn4+4uDgolUpcv34dixYtwowZM5pVTGFhIS5evIhHHnkE/fr1g1KpxI4dOzBx4kQAQGpqKjIyMhAfH9+s9RNR46hdlOgX5o1+Yd7WaYIgIEtfirNZBqTmFOBstrn15tK1QtwsrqjVuVguA8J93c3BploLTkcvV7beEFGLadapJV9fX+zevRs9evTAV199hU8//RTHjx/HDz/8gNmzZ+Ps2bONWs8rr7yC0aNHIywsDFlZWZgzZw5OnDiBM2fOwM/PDzNmzMCmTZuwYsUKaDQazJo1CwCwb9++RtfK68gQta7SCiMu5BZag02KzoCz2QXIKyqvc3m1SoHIwKqh4RpEB5lHUHmomvV3FRE5qFY9tVRcXAy12jxS4pdffsGECRMgl8sxaNAgpKenN3o9V65cwcMPP4wbN27Az88Pd911Fw4cOAA/Pz8AwEcffQS5XI6JEyfaXBCPiOyHi9IJPTto0bPDrc7FgiDgWmHZrX432QU4qyvAhVzz6akj6TdxJP2mzXpCvF3NwaZa602YjzucODSciBrQrBaZXr164cknn8T48ePRs2dPbNmyBfHx8Th69ChGjRrV4KiitsYWGSL7UWE04dK1ImurTVXI0RlK61zeRSlHZIB55FRVH5yoQA283Tk0nMjRteotCr7//nv8+c9/htFoxLBhw7Bt2zYA5qHPv/32GzZv3tz8ylsYgwyR/btZVI4UnbnfTdW1b1JzClBaUfetGfzVKkQFVbXemMNNFz8ODSdyJK1+92udTofs7GzExsZCLjf/53Ho0CFoNBpERUU1r+pWwCBDJE1Gk4B069Bwg3W0VEZecZ3LK+SWoeGWYBNlCTmBGt5Yk0iKWj3IVLly5QoAWO+EbW8YZIgcS2FZ5a1WG8uw8LM6AwpKK+tcXuuqtF7vJjJQje6WnxwaTmTfWjXImEwmvPPOO/jwww9RWFgIAFCr1Xj55Zfx+uuvW1to7AGDDJHjqxoaXr3lJiXbgEvXi2A01f4vTiGXoVuAGj07aBBj6agcHaRhuCGyI606aun111/HsmXL8P7772PIkCEAgN9//x1z585FaWkp3n333eZVTUTUDDKZDB08XdHB0xXDo2/dObxqaHiKrgCplg7GyVl65BdX4Ey2AWeyDfjuiLlV2UkuQ4S/h3kEVrAGMR3N4cbNmcPCiexZs1pkgoODsXTpUutdr6v8+OOPeOaZZ3D16tUWK/BOsUWGiKoTBAFX80uQfFWP5KsGJF3VI/mqHjfquO6NXAZ08fNATActenTQIqaDFt2DNbzmDVEbaNUWmby8vDo79EZFRSEvL685qyQiahMymQwdvdzQ0csNI3oGATCHG52hFElX9EjOMiD5qh5JV/W4VlCG87mFOJ9biLXHr1reD3TydUeMJdj0CNaiRwcNNC5KMTeLqN1qVotMXFwc4uLi8Mknn9hMnzVrFg4dOoSDBw+2WIF3ii0yRNRcuYZSS4vNrZab+q55E+7jhp6WcFPVgqN1Zbghaq5W7ey7e/dujBo1CqGhodb7Hu3fvx+ZmZnYtGkThg4d2vzKWxiDDBG1pGsFZUjO0uO0pdUm+aoBV/Prvlt4WI1w0zNYC60bww1RY7T68OusrCx89tlnSElJAQBER0dj+vTpeOedd/DFF180r+pWwCBDRK0tr6gcp7P01labpKt6ZObVHW5Cvd2sI6XMPzXwdOOViolqarPryFR38uRJ9O3bF0ajsaVWeccYZIhIDDeLypHcyHAT4u1qG26CtfDibRionWvVzr5ERNQwL3dnDI3ww9AIP+u0/OJym/42SVf1yMgrRmZeCTLzSrAp6dZ96jp62YabmA4MN0R1YZAhImojnm7OuCvCF3dF+Fqn6YsrrC03VQEn/UYxrtwswZWbJdicfCvcdPB0RfdgDboHaaw/O3q58hYM1K4xyBARiUjrpsSQrr4Y0rVauCmpsHYmrgo3l28U42p+Ca7ml2DbmRzrsmoXBaKDbMNNRIAHVApepZjahyYFmQkTJjQ4Pz8//05qISIimO8PNbirLwbXCDdnsgw4a7ki8dlsA87lFKCgtBKH0vJwKO3WNbwUchm6+nvYhJvoIA1PTZFDalJn38cee6xRyy1fvrzZBbU0dvYlIkdVXmnCxWuFOJN1K9yczjJAX1JR5/LBWhebU1PRQRqEeLlBLuepKbI/ooxaskcMMkTUngiCgGx9qTXcnMky4KzOgPQbxXUu76FSIDpIbQ03PTto0S1ADaWT/dz8l9onBhkLBhkiIqCgtAIpugJzwLGEnNScApRXmmot66yQIzpQbb6BpmXEVLcANZwVDDfUdhhkLBhkiIjqVmk04dL1IpzJMuB0lh6nLfeZMpRW1lrW2UmOSGu40SCmgxaRgWp2KqZWwyBjwSBDRNR4giAgI6/YevuFquvd1NXvRukkQ7cAtfV6Nz07aBEVqIaLkuGG7hyDjAWDDBHRnREEAVdultgMB0++qsfN4trhRiGXISJAjRhLq02PDlp0D9Iw3FCTMchYMMgQEbU8QRBwNb/E2mKTZGm9ySsqr7Wsk1yGCH8P9An1xIBwbwwI9+aF/Oi2GGQsGGSIiNpG1Yip6rdgSL6qx/XC2uEmQKNC/3BvDAz3Rv9wL0QFauDEYeBUDYOMBYMMEZF4BEFAjqEMJ6/k41j6TRy+nIekq3pUGG0PPR4qBfqGeWFAmBf6h3ujd4gnXJ15Oqo9Y5CxYJAhIrIvJeVGnLySjyOX83D48k0cS7+JgjLbkVJKJxl6dtBiQLg3+lvCjTevTNyuMMhYMMgQEdk3o0lAis6AI5fNLTaHL+chx1BWa7mu/h4YEO6F/mHmfjYh3uxn48gYZCwYZIiIpKVqlNRhS4vNkct5OJ9bWGu5qn42VaejooPYz8aRMMhYMMgQEUnfzaJyHEm/aTkdVXc/G42LAoM6+yC+iw8Gd/FFtwAPtthIGIOMBYMMEZHjKa0w4mRmvrXV5mj6TRTW6Gfj4+6MQV18EN/ZB4O7+KCTrzuDjYQwyFgwyBAROb5KownJWQbsu3gd+y/ewOHLeSitsL2PVIBGhcFdfBFvabUJ8XYTqVpqDAYZCwYZIqL2p7zShJNX8rHvwg3sv3Qdx9LzUW60DTYdvVwxuIs51MR39kWg1kWkaqkuDDIWDDJERFRaYcSx9JvYd/EG9l+6gZOZ+ag02R7+Ovu6m0NNFx8M6uwDXw+VSNUSwCBjxSBDREQ1FZVV4vDlPOy3BJvkq3rUyDWIDFDfCjadfKB1U4pTbDvFIGPBIENERLejL6nAobQ8ax+bFF2BzXyZDOgepMGgzubWmoHh3gw2rYxBxoJBhoiImupGYRkOWoLNvos3cOlakc18BpvWxyBjwSBDRER3KtdQigNpeThw6QYOXGKwaQsMMhYMMkRE1NIYbFofg4wFgwwREbU2BpuWxyBjwSBDRERtranBZkC4FzzdeHfv6hhkLBhkiIhIbLcLNgAQ4e+B/uFe6Bfmjf5hXgjzcWvXt1RgkLFgkCEiInvTmGDj66FCvzBP9A/zRr9wL/QM1sJZIRehWnEwyFgwyBARkb27UViGo+nmm18eSb+JpCv6WrdUUCnkiO3oiX7hXugf5oV+YY59OopBxoJBhoiIpKa0wojkq3rLnb3zcDT9Jm4WV9Rarqu/BwY46OkoBhkLBhkiIpI6QRBw8VoRjqbn4chlc8vNpet1nY5yRr8wL4c4HcUgY8EgQ0REjqgpp6PuivDFsCh/9AjWSKbFhkHGgkGGiIjag6rTUUfSb1pabfJqnY7yV6twb6Q/hkX7466uvnBXKUSq9vYYZCwYZIiIqD0SBAGXrhfhUFoedqbk4vcL11FcbrTOd3aSI66ztznYRPkj3NddxGprY5CxYJAhIiICyiqNOHgpD7+m5GJnai7SbxTbzO/s6457o8yhZkC4t+h9axhkLBhkiIiIbFW11uxMycWvKbk4lJaHStOtOOChUmBohC/ujfLHPZF+8Fe7tHmNDDIWDDJEREQNKyitwO/nr1tba64XltvM79VRaz0FFdNBC7m89TsMM8hYMMgQERE1nskkIOmq3hpqTl3R28z39VDh3kg/DIvyx10RvlC7tM7NLxlkLBhkiIiImi+3oBS7Uq9hZ0ou9py/jsKySus8pZMMA8K98fiQTkjoHtCin9vY47f9jrsiIiIi0fmrXfBg/xA82D8E5ZUmHL5s6TCckotL14uw7+INjOvdQbT62CJDREREzXL5ehF+TcnF6Nhg+KlVLbputsgQERFRqwr3dcfjd3UStQa7uQHD+++/D5lMhhdeeME6rbS0FDNnzoSPjw88PDwwceJE5OTkiFckERER2RW7CDKHDx/G559/jl69etlMf/HFF/HTTz9hzZo12L17N7KysjBhwgSRqiQiIiJ7I3qQKSwsxJQpU/Dll1/Cy8vLOl2v12PZsmVYtGgRhg0bhn79+mH58uXYt28fDhw4IGLFREREZC9EDzIzZ87EqFGjkJCQYDP96NGjqKiosJkeFRWF0NBQ7N+/v971lZWVwWAw2DyIiIjIMYna2Xf16tU4duwYDh8+XGueTqeDs7MzPD09baYHBARAp9PVu8758+dj3rx5LV0qERER2SHRWmQyMzPx/PPP49tvv4WLS8vdw+G1116DXq+3PjIzM1ts3URERGRfRAsyR48eRW5uLvr27QuFQgGFQoHdu3fjk08+gUKhQEBAAMrLy5Gfn2/zvpycHAQGBta7XpVKBY1GY/MgIiIixyTaqaXhw4cjKSnJZtpjjz2GqKgovPrqqwgJCYFSqcSOHTswceJEAEBqaioyMjIQHx8vRslERERkZ0QLMmq1Gj179rSZ5u7uDh8fH+v0J554Ai+99BK8vb2h0Wgwa9YsxMfHY9CgQWKUTERERHbGrq/s+9FHH0Eul2PixIkoKytDYmIiFi9eLHZZREREZCd4ryUiIiKyO409fot+HRkiIiKi5mKQISIiIslikCEiIiLJYpAhIiIiyWKQISIiIslikCEiIiLJYpAhIiIiyWKQISIiIslikCEiIiLJYpAhIiIiyWKQISIiIslikCEiIiLJYpAhIiIiyWKQISIiIslikCEiIiLJYpAhIiIiyWKQISIiIslikCEiIiLJYpAhIiIiyWKQISIiIslikCEiIiLJYpAhIiIiyWKQISIiIslikCEiIiLJYpAhIiIiyWKQISIiIslikCEiIiLJYpAhIiIiyWKQISIiIslikCEiIiLJYpAhIiIiyWKQISIiIslikCEiIiLJYpAhIiIiyWKQISIiIslikCEiIiLJYpAhIiIiyWKQISIiIslikCEiIiLJYpAhIiIiyWKQISIiIslikCEiIiLJYpAhIiIiyWKQISIiIslikCEiIiLJYpAhIiIiyWKQISIiIslikCEiIiLJYpAhIiIiyWKQISIiIslikLkTRTfEroCIiKhdY5BprqTvgX/2As5vE7sSIiKidotBpjkEwRxkyguBlZOB49+KXREREVG7xCDTHDIZ8OB/gF4PAYIR+PEZ4LeF5oBDREREbYZBprkUzsD4pcBdL5pf//oOsOkVwGQUty4iIqJ2RNQgs2TJEvTq1QsajQYajQbx8fHYvHmzdX5paSlmzpwJHx8feHh4YOLEicjJyRGx4hpkMiBhLjByIQAZcPgr4LtHgYoSsSsjIiJqF0QNMh07dsT777+Po0eP4siRIxg2bBjGjh2L06dPAwBefPFF/PTTT1izZg12796NrKwsTJgwQcyS6xY3HfjTCsBJBaT8DPxnHFCcJ3ZVREREDk8mCPbVscPb2xsLFy7EpEmT4Ofnh5UrV2LSpEkAgJSUFERHR2P//v0YNGhQne8vKytDWVmZ9bXBYEBISAj0ej00Gk3rFn95L7DqYaBMD/hGAn/5AfAMad3PJCIickAGgwFarfa2x2+76SNjNBqxevVqFBUVIT4+HkePHkVFRQUSEhKsy0RFRSE0NBT79++vdz3z58+HVqu1PkJC2jBIhA8BHt8CqIOB66nAsvsAXXLbfT4REVE7I3qQSUpKgoeHB1QqFZ5++mmsW7cO3bt3h06ng7OzMzw9PW2WDwgIgE6nq3d9r732GvR6vfWRmZnZyltQQ0B34MltgF80UJANLB8JpO1p2xqIiIjaCdGDTGRkJE6cOIGDBw9ixowZmDp1Ks6cOdPs9alUKmvn4apHm9N2BB7fDIQOBsoMwDcTgOQf2r4OIiIiByd6kHF2dkbXrl3Rr18/zJ8/H7GxsfjnP/+JwMBAlJeXIz8/32b5nJwcBAYGilNsU7h6AY+sA6LHAMZy4PvHgf2Lxa6KiIjIoYgeZGoymUwoKytDv379oFQqsWPHDuu81NRUZGRkID4+XsQKm0DpYh7NNHC6+fXW14Bf3gBMJlHLIiIichQKMT/8tddew8iRIxEaGoqCggKsXLkSu3btwtatW6HVavHEE0/gpZdegre3NzQaDWbNmoX4+Ph6RyzZJbkTMHIBoAkGts8F9n0KFOiAsYvNF9UjIiKiZhM1yOTm5uLRRx9FdnY2tFotevXqha1bt+K+++4DAHz00UeQy+WYOHEiysrKkJiYiMWLJXh6RiYzXwFYHQT8OBNIWgMU5gKTvwFcROjDQ0RE5CDs7joyLa2x49DbzIXtwP8eBSqKgMAYYMr3gFoCfX6IiIjakOSuI9NudE0AHtsIuPsBuiTztWaunxe7KiIiIklikBFDcB/giW2Ad2cgP8McZjIPiV0VERGR5DDIiMW7kznMBPcFSm4C/x4DpGwSuyoiIiJJYZARk7svMO1nIOJ+oLIE+N8U4OgKsasiIiKSDAYZsTm7Aw+tAvr8BRBMwE/PAzvnA47dB5uIiKhFMMjYAycFMOZfwN1/M7/e/T7w03OAsVLcuoiIiOwcg4y9kMmAYa8Df/wIkMmBY/8xn2oqKxC7MiIiIrvFIGNv+j9uvlCewgU4twX4KgG4cVHsqoiIiOwSg4w9ihoFTNtovhLwtRTgi3uBc1vFroqIiMjuMMjYq479gem7gZBBQJkeWDkZ2L2AN5wkIiKqhkHGnqkDgKk/Af2fACAAO98FvnsEKDWIXRkREZFdYJCxdwpn4I+LgDGfAk7OQMrPwFfDeVsDIiIiMMhIR99Hgce2AOpg4Po54MthvBIwERG1ewwyUtKxHzB9FxAaD5QZgNUPA7veZ78ZIiJqtxhkpEYdADy6ARg43fx613zz9WZK9eLWRUREJAIGGSlSOAMPLATGLgacVEDqJuDL4cC1c2JXRkRE1KYYZKSszxTg8c2ApgNw47y538zZn8WuioiIqM0wyEhdh37m682EDQHKC8ynmX59l/1miIioXWCQcQQefsCjPwJxT5tf/7bA3BG4JF/UsoiIiFobg4yjcFICIz8Axi0195s5t8V8qik3RezKiIiIWg2DjKPp/TDwxFZA0xHIu2i+eN6ZDWJXRURE1CoYZBxRcB/g/+0GwocC5YXm2xrseBswGcWujIiIqEUxyDgqd1/gkfXAoJnm13v+Aax6iP1miIjIoTDIODInBTDiPWD8F4DCBTj/C/DlvUDOGbErIyIiahEMMu1B7GTg8a2ANhTIuwR8lQCcXi92VURERHeMQaa9CO5tvk9Tp7uBiiJgzVRg/Uzg/HagolTs6oiIiJpFJgiCIHYRrclgMECr1UKv10Oj0YhdjviMlcD2OcD+f92apnQDOv0B6HY/EJEIaDuIVx8REREaf/xmkGmvLu0GTq8Fzv0CFGTZzguIuRVqOvYH5E7i1EhERO0Wg4wFg8xtCAKgSwLObzWHmiuHAVT7lXD1BromAN0Sga7DAVcv0UolIqJqTCbAWAZUlgKV5eafxvIar8vMz4Wq29ZY/n+3OfQ3Zlq1eXVNC+4DeHdukc2qwiBjwSDTREU3gAvbzVcGvrgDKNXfmidzAkLibrXW+EcDMpl4tRIR2QNjBVBRYn5Ulpj7Hdb8WVFsDhYVJZafNeeVWUJH1aOuUFLjtalC7C2/5Y8fA/0fa9FVMshYMMjcAWMlkHnwVmvNtbO287Wht0JNp6GA0lWcOolI2kxGwFRpDgSmCvNr6/NK8/9FpgrLNGO155XV3le1TGW153UtU+1zbJatrPZ5dbzPWF4trJTa/hTs4WKjMvNlNhTOlp8q8+1qFC7mW9jYdBGo9geo9Y/RuqY1Ydn4mUDkyDvfjGoYZCwYZFrQzXTztWjObQUu7zF/iasoXM0joqqCjWeIeHUSSYEgmA+SxnLLo67ndUyrOvBaD/aNeV1Z48BsrHHQNt56DwTzaQjBZK7R+ryu19Wmoa55VQ+Yf9YMJlWfDwc6DClcAaWLeRCFwsX8B57NzwbmKVTVAkjVwwVwcradXz2kVAUXucLhWsgZZCwYZFpJeTGQ9tut1hrDFdv5/t3N50tdPc39alw8azz3Mr928QRctOxQ7EhMVQcvo+XgZaz2Wqjxuvp8y6NRB9war2sesOtdtvLW5wp1HLBrHYxrvAbqP5BXPRoKITaBxI5OC9gjmZP54OyktP0pV5ov9imvml79ec1lW/B9Ts63Qoo1rLjaTlOoHC5MiIlBxoJBpg0IApB7xtyv5twvwJVD1TqWNYYMcNHUE3g86wk/GkDWipdBqjrAmozmA1/1n42eVlntQF3XNJPtewSTJQQY65hvqr0um2k11yXUWG/V+0x1rLeu6TXqqb4O6/R6wgo1k8zyl7az+SBq89PyXK60PdhaD7hONQ7K1V5bD9o1D+JVr51urVPmZP5eyeTmA7LNz2oPyOpY7nbLonb99YUMuQKQ8zJn7R2DjAWDjAiK84DLvwNFueZ7O5XcBErzLc/zLc9vmp9XFIlZKYnGcnCTVx04LT/llud1HqxrHHRrHQxrHBjrWrbqUddBGjUPwjUO0LXm13HgBmyDh5OzJZwoa0+v+ZytkkQ2Gnv8VrRhTdReuHkD3cc0btnK8lshp3rAsQk/dTwvK6gxVLAVVP1VKHOy/IVr+YvVOk1hORBbplnnO1V7j9Ntpslt12k9uFefX22azXNZ49dlExpqBAi5ZV211lPP8g2tq9Hz2PxORC2DQYbEpXAGPPzNDyIioibiSUgiIiKSLAYZIiIikiwGGSIiIpIsBhkiIiKSLAYZIiIikiwGGSIiIpIsBhkiIiKSLAYZIiIikiwGGSIiIpIsBhkiIiKSLAYZIiIikiwGGSIiIpIsBhkiIiKSLAYZIiIikiyF2AW0NkEQAAAGg0HkSoiIiKixqo7bVcfx+jh8kCkoKAAAhISEiFwJERERNVVBQQG0Wm2982XC7aKOxJlMJmRlZUGtVkMmk7XYeg0GA0JCQpCZmQmNRtNi67Unjr6Njr59gONvI7dP+hx9G7l9zScIAgoKChAcHAy5vP6eMA7fIiOXy9GxY8dWW79Go3HIX87qHH0bHX37AMffRm6f9Dn6NnL7mqehlpgq7OxLREREksUgQ0RERJLFINNMKpUKc+bMgUqlEruUVuPo2+jo2wc4/jZy+6TP0beR29f6HL6zLxERETkutsgQERGRZDHIEBERkWQxyBAREZFkMcgQERGRZDHINOCzzz5DeHg4XFxcEBcXh0OHDjW4/Jo1axAVFQUXFxfExMRg06ZNbVRp082fPx8DBgyAWq2Gv78/xo0bh9TU1Abfs2LFCshkMpuHi4tLG1XcNHPnzq1Va1RUVIPvkdL+A4Dw8PBa2yiTyTBz5sw6l7f3/ffbb79h9OjRCA4Ohkwmw/r1623mC4KA2bNnIygoCK6urkhISMD58+dvu96mfo9bS0PbV1FRgVdffRUxMTFwd3dHcHAwHn30UWRlZTW4zub8nrem2+3DadOm1ap3xIgRt12vFPYhgDq/jzKZDAsXLqx3nfa0DxtzXCgtLcXMmTPh4+MDDw8PTJw4ETk5OQ2ut7nf3cZikKnH//73P7z00kuYM2cOjh07htjYWCQmJiI3N7fO5fft24eHH34YTzzxBI4fP45x48Zh3LhxSE5ObuPKG2f37t2YOXMmDhw4gG3btqGiogL3338/ioqKGnyfRqNBdna29ZGent5GFTddjx49bGr9/fff611WavsPAA4fPmyzfdu2bQMA/OlPf6r3Pfa8/4qKihAbG4vPPvuszvkLFizAJ598gqVLl+LgwYNwd3dHYmIiSktL611nU7/Hramh7SsuLsaxY8fw5ptv4tixY1i7di1SU1MxZsyY2663Kb/nre12+xAARowYYVPvqlWrGlynVPYhAJvtys7Oxtdffw2ZTIaJEyc2uF572YeNOS68+OKL+Omnn7BmzRrs3r0bWVlZmDBhQoPrbc53t0kEqtPAgQOFmTNnWl8bjUYhODhYmD9/fp3LP/jgg8KoUaNspsXFxQn/7//9v1ats6Xk5uYKAITdu3fXu8zy5csFrVbbdkXdgTlz5gixsbGNXl7q+08QBOH5558XunTpIphMpjrnS2n/ARDWrVtnfW0ymYTAwEBh4cKF1mn5+fmCSqUSVq1aVe96mvo9bis1t68uhw4dEgAI6enp9S7T1N/ztlTXNk6dOlUYO3Zsk9Yj5X04duxYYdiwYQ0uY8/7sOZxIT8/X1AqlcKaNWusy5w9e1YAIOzfv7/OdTT3u9sUbJGpQ3l5OY4ePYqEhATrNLlcjoSEBOzfv7/O9+zfv99meQBITEysd3l7o9frAQDe3t4NLldYWIiwsDCEhIRg7NixOH36dFuU1yznz59HcHAwOnfujClTpiAjI6PeZaW+/8rLy/HNN9/g8ccfb/DmqFLaf9WlpaVBp9PZ7COtVou4uLh691Fzvsf2RK/XQyaTwdPTs8HlmvJ7bg927doFf39/REZGYsaMGbhx40a9y0p5H+bk5GDjxo144oknbrusve7DmseFo0ePoqKiwmZ/REVFITQ0tN790ZzvblMxyNTh+vXrMBqNCAgIsJkeEBAAnU5X53t0Ol2TlrcnJpMJL7zwAoYMGYKePXvWu1xkZCS+/vpr/Pjjj/jmm29gMpkwePBgXLlypQ2rbZy4uDisWLECW7ZswZIlS5CWloahQ4eioKCgzuWlvP8AYP369cjPz8e0adPqXUZK+6+mqv3QlH3UnO+xvSgtLcWrr76Khx9+uMEb8TX191xsI0aMwH/+8x/s2LEDH3zwAXbv3o2RI0fCaDTWubyU9+G///1vqNXq2552sdd9WNdxQafTwdnZuVa4vt2xsWqZxr6nqRz+7td0ezNnzkRycvJtz8vGx8cjPj7e+nrw4MGIjo7G559/jrfffru1y2ySkSNHWp/36tULcXFxCAsLw3fffdeov5CkZtmyZRg5ciSCg4PrXUZK+689q6iowIMPPghBELBkyZIGl5Xa7/lDDz1kfR4TE4NevXqhS5cu2LVrF4YPHy5iZS3v66+/xpQpU27bod5e92Fjjwv2gC0ydfD19YWTk1Otntg5OTkIDAys8z2BgYFNWt5ePPvss/j555+xc+dOdOzYsUnvVSqV6NOnDy5cuNBK1bUcT09PdOvWrd5apbr/ACA9PR3bt2/Hk08+2aT3SWn/Ve2Hpuyj5nyPxVYVYtLT07Ft27YGW2Pqcrvfc3vTuXNn+Pr61luvFPchAOzZswepqalN/k4C9rEP6zsuBAYGory8HPn5+TbL3+7YWLVMY9/TVAwydXB2dka/fv2wY8cO6zSTyYQdO3bY/EVbXXx8vM3yALBt27Z6lxebIAh49tlnsW7dOvz666/o1KlTk9dhNBqRlJSEoKCgVqiwZRUWFuLixYv11iq1/Vfd8uXL4e/vj1GjRjXpfVLaf506dUJgYKDNPjIYDDh48GC9+6g532MxVYWY8+fPY/v27fDx8WnyOm73e25vrly5ghs3btRbr9T2YZVly5ahX79+iI2NbfJ7xdyHtzsu9OvXD0ql0mZ/pKamIiMjo9790ZzvbnMKpzqsXr1aUKlUwooVK4QzZ84I06dPFzw9PQWdTicIgiA88sgjwt///nfr8nv37hUUCoXwj3/8Qzh79qwwZ84cQalUCklJSWJtQoNmzJghaLVaYdeuXUJ2drb1UVxcbF2m5jbOmzdP2Lp1q3Dx4kXh6NGjwkMPPSS4uLgIp0+fFmMTGvTyyy8Lu3btEtLS0oS9e/cKCQkJgq+vr5CbmysIgvT3XxWj0SiEhoYKr776aq15Utt/BQUFwvHjx4Xjx48LAIRFixYJx48ft47aef/99wVPT0/hxx9/FE6dOiWMHTtW6NSpk1BSUmJdx7Bhw4RPP/3U+vp232N72b7y8nJhzJgxQseOHYUTJ07YfCfLysrq3b7b/Z63tYa2saCgQHjllVeE/fv3C2lpacL27duFvn37ChEREUJpaal1HVLdh1X0er3g5uYmLFmypM512PM+bMxx4emnnxZCQ0OFX3/9VThy5IgQHx8vxMfH26wnMjJSWLt2rfV1Y767d4JBpgGffvqpEBoaKjg7OwsDBw4UDhw4YJ33hz/8QZg6darN8t99953QrVs3wdnZWejRo4ewcePGNq648QDU+Vi+fLl1mZrb+MILL1j/PQICAoQHHnhAOHbsWNsX3wiTJ08WgoKCBGdnZ6FDhw7C5MmThQsXLljnS33/Vdm6dasAQEhNTa01T2r7b+fOnXX+TlZtg8lkEt58800hICBAUKlUwvDhw2ttd1hYmDBnzhybaQ19j9tSQ9uXlpZW73dy586d1nXU3L7b/Z63tYa2sbi4WLj//vsFPz8/QalUCmFhYcJTTz1VK5BIdR9W+fzzzwVXV1chPz+/znXY8z5szHGhpKREeOaZZwQvLy/Bzc1NGD9+vJCdnV1rPdXf05jv7p2QWT6UiIiISHLYR4aIiIgki0GGiIiIJItBhoiIiCSLQYaIiIgki0GGiIiIJItBhoiIiCSLQYaIiIgki0GGiIiIJItBhojaHZlMhvXr14tdBhG1AAYZImpT06ZNg0wmq/UYMWKE2KURkQQpxC6AiNqfESNGYPny5TbTVCqVSNUQkZSxRYaI2pxKpUJgYKDNw8vLC4D5tM+SJUswcuRIuLq6onPnzvj+++9t3p+UlIRhw4bB1dUVPj4+mD59OgoLC22W+frrr9GjRw+oVCoEBQXh2WeftZl//fp1jB8/Hm5uboiIiMCGDRtad6OJqFUwyBCR3XnzzTcxceJEnDx5ElOmTMFDDz2Es2fPAgCKioqQmJgILy8vHD58GGvWrMH27dttgsqSJUswc+ZMTJ8+HUlJSdiwYQO6du1q8xnz5s3Dgw8+iFOnTuGBBx7AlClTkJeX16bbSUQtoMXuo01E1AhTp04VnJycBHd3d5vHu+++KwiCIAAQnn76aZv3xMXFCTNmzBAEQRC++OILwcvLSygsLLTO37hxoyCXywWdTicIgiAEBwcLr7/+er01ABDeeOMN6+vCwkIBgLB58+YW204iahvsI0NEbe7ee+/FkiVLbKZ5e3tbn8fHx9vMi4+Px4kTJwAAZ8+eRWxsLNzd3a3zhwwZApPJhNTUVMhkMmRlZWH48OEN1tCrVy/rc3d3d2g0GuTm5jZ3k4hIJAwyRNTm3N3da53qaSmurq6NWk6pVNq8lslkMJlMrVESEbUi9pEhIrtz4MCBWq+jo6MBANHR0Th58iSKioqs8/fu3Qu5XI7IyEio1WqEh4djx44dbVozEYmDLTJE1ObKysqg0+lspikUCvj6+gIA1qxZg/79++Ouu+7Ct99+i0OHDmHZsmUAgClTpmDOnDmYOnUq5s6di2vXrmHWrFl45JFHEBAQAACYO3cunn76afj7+2PkyJEoKCjA3r17MWvWrLbdUCJqdQwyRNTmtmzZgqCgIJtpkZGRSElJAWAeUbR69Wo888wzCAoKwqpVq9C9e3cAgJubG7Zu3Yrnn38eAwYMgJubGyZOnIhFixZZ1zV16lSUlpbio48+wiuvvAJfX19MmjSp7TaQiNqMTBAEQewiiIiqyGQyrFu3DuPGjRO7FCKSAPaRISIiIslikCEiIiLJYh8ZIrIrPNtNRE3BFhkiIiKSLAYZIiIikiwGGSIiIpIsBhkiIiKSLAYZIiIikiwGGSIiIpIsBhkiIiKSLAYZIiIikqz/D9vh60aft18JAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_loss(train_all_loss2,test_all_loss2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ea3e33d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_acc(train_Acc12,test_Acc12)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "166d1f6e",
   "metadata": {},
   "source": [
    "## 手动实现前馈神经网络多分类任务"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "140f1704",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 多分类任务\n",
    "traindataset3 = torchvision.datasets.FashionMNIST(root='./FashionMNIST', train=True, download=True, transform=transforms.ToTensor())\n",
    "testdataset3 = torchvision.datasets.FashionMNIST(root='./FashionMNIST', train=False, download=True, transform=transforms.ToTensor())\n",
    "batch_size = 256\n",
    "traindataloader3 = torch.utils.data.DataLoader(traindataset3, batch_size=batch_size, shuffle=True, num_workers=0)\n",
    "testdataloader3 = torch.utils.data.DataLoader(testdataset3, batch_size=batch_size, shuffle=False, num_workers=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "54b97683",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义自己的前馈神经网络\n",
    "class MyNet3():\n",
    "    def __init__(self):\n",
    "        # 设置隐藏层和输出层的节点数\n",
    "        num_inputs, num_hiddens, num_outputs = 28 * 28, 256, 10  # 十分类问题\n",
    "        w_1 = torch.tensor(np.random.normal(0, 0.01, (num_hiddens, num_inputs)), dtype=torch.float32,\n",
    "                           requires_grad=True)\n",
    "        b_1 = torch.zeros(num_hiddens, dtype=torch.float32, requires_grad=True)\n",
    "        w_2 = torch.tensor(np.random.normal(0, 0.01, (num_outputs, num_hiddens)), dtype=torch.float32,\n",
    "                           requires_grad=True)\n",
    "        b_2 = torch.zeros(num_outputs, dtype=torch.float32, requires_grad=True)\n",
    "        self.params = [w_1, b_1, w_2, b_2]\n",
    "\n",
    "        # 定义模型结构\n",
    "        self.input_layer = lambda x: x.view(x.shape[0], -1)\n",
    "        self.hidden_layer = lambda x: self.my_relu(torch.matmul(x, w_1.t()) + b_1)\n",
    "        self.output_layer = lambda x: torch.matmul(x, w_2.t()) + b_2\n",
    "\n",
    "    def my_relu(self, x):\n",
    "        return torch.max(input=x, other=torch.tensor(0.0))\n",
    "\n",
    "    # 定义前向传播\n",
    "    def forward(self, x):\n",
    "        x = self.input_layer(x)\n",
    "        x = self.hidden_layer(x)\n",
    "        x = self.output_layer(x)\n",
    "        return x\n",
    "\n",
    "\n",
    "def mySGD(params, lr, batchsize):\n",
    "    for param in params:\n",
    "        param.data -= lr * param.grad / batchsize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "61da281e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 1 | train loss:539.73 | test loss:91.53 | train acc: 0.189 | test acc: 0.322\n",
      "epoch: 2 | train loss:535.51 | test loss:90.76 | train acc: 0.341 | test acc: 0.351\n",
      "epoch: 3 | train loss:530.27 | test loss:89.73 | train acc: 0.359 | test acc: 0.351\n",
      "epoch: 4 | train loss:523.09 | test loss:88.32 | train acc: 0.356 | test acc: 0.355\n",
      "epoch: 5 | train loss:513.28 | test loss:86.40 | train acc: 0.367 | test acc: 0.374\n",
      "epoch: 6 | train loss:500.23 | test loss:83.90 | train acc: 0.396 | test acc: 0.421\n",
      "epoch: 7 | train loss:483.66 | test loss:80.80 | train acc: 0.452 | test acc: 0.463\n",
      "epoch: 8 | train loss:463.85 | test loss:77.22 | train acc: 0.477 | test acc: 0.472\n",
      "epoch: 9 | train loss:441.94 | test loss:73.41 | train acc: 0.490 | test acc: 0.472\n",
      "epoch: 10 | train loss:419.45 | test loss:69.65 | train acc: 0.496 | test acc: 0.484\n",
      "epoch: 11 | train loss:397.95 | test loss:66.13 | train acc: 0.507 | test acc: 0.527\n",
      "epoch: 12 | train loss:378.08 | test loss:62.93 | train acc: 0.544 | test acc: 0.565\n",
      "epoch: 13 | train loss:360.06 | test loss:60.04 | train acc: 0.580 | test acc: 0.586\n",
      "epoch: 14 | train loss:343.85 | test loss:57.44 | train acc: 0.600 | test acc: 0.593\n",
      "epoch: 15 | train loss:329.22 | test loss:55.10 | train acc: 0.609 | test acc: 0.602\n",
      "epoch: 16 | train loss:316.13 | test loss:52.99 | train acc: 0.616 | test acc: 0.611\n",
      "epoch: 17 | train loss:304.26 | test loss:51.09 | train acc: 0.624 | test acc: 0.619\n",
      "epoch: 18 | train loss:293.47 | test loss:49.36 | train acc: 0.630 | test acc: 0.625\n",
      "epoch: 19 | train loss:283.72 | test loss:47.79 | train acc: 0.637 | test acc: 0.628\n",
      "epoch: 20 | train loss:274.84 | test loss:46.37 | train acc: 0.642 | test acc: 0.629\n",
      "手动实现前馈网络-多分类实验 20轮 总用时: 184.44s\n"
     ]
    }
   ],
   "source": [
    "# 训练\n",
    "model3 = MyNet3()  # logistics模型\n",
    "criterion = my_cross_entropy_loss  # 损失函数\n",
    "lr = 0.15  # 学习率\n",
    "epochs = 20  # 训练轮数\n",
    "train_all_loss3 = []  # 记录训练集上得loss变化\n",
    "test_all_loss3 = []  # 记录测试集上的loss变化\n",
    "train_ACC13, test_ACC13 = [], [] # 记录正确的个数\n",
    "begintime3 = time.time()\n",
    "for epoch in range(epochs):\n",
    "    train_l,train_acc_num = 0, 0\n",
    "    for data, labels in traindataloader3:\n",
    "        pred = model3.forward(data)\n",
    "        train_each_loss = criterion(pred, labels)  # 计算每次的损失值\n",
    "        train_l += train_each_loss.item()\n",
    "        train_each_loss.backward()  # 反向传播\n",
    "        mySGD(model3.params, lr, 128)  # 使用小批量随机梯度下降迭代模型参数\n",
    "        # 梯度清零\n",
    "        train_acc_num += (pred.argmax(dim=1)==labels).sum().item()\n",
    "        for param in model3.params:\n",
    "            param.grad.data.zero_()\n",
    "        # print(train_each_loss)\n",
    "    train_all_loss3.append(train_l)  # 添加损失值到列表中\n",
    "    train_ACC13.append(train_acc_num / len(traindataset3)) # 添加准确率到列表中\n",
    "    with torch.no_grad():\n",
    "        test_l, test_acc_num = 0, 0\n",
    "        for data, labels in testdataloader3:\n",
    "            pred = model3.forward(data)\n",
    "            test_each_loss = criterion(pred, labels)\n",
    "            test_l += test_each_loss.item()\n",
    "            test_acc_num += (pred.argmax(dim=1)==labels).sum().item()\n",
    "        test_all_loss3.append(test_l)\n",
    "        test_ACC13.append(test_acc_num / len(testdataset3))   # # 添加准确率到列表中\n",
    "        print('epoch: %d | train loss:%.2f | test loss:%.2f | train acc: %.3f | test acc: %.3f'\n",
    "              % (epoch + 1, train_l, test_l, train_ACC13[-1],test_ACC13[-1]))\n",
    "endtime3 = time.time()\n",
    "print(\"手动实现前馈网络-多分类实验 %d轮 总用时: %.2fs\" % (epochs, endtime3 - begintime3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "374fcb62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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UanWza/j3v/+N+fPn49FHHwUAvPvuu/jjjz/wwQcf4OOPP260hvT0dKjVasTFxUEulyM4OBgDBgxodi13wqqnsRYtWnRLZ6xu3bqZlldUVCA+Ph7e3t5wc3PDpEmTkJOTY7aO9PR0jBkzBi4uLvDz88NLL72E6urqtt4UskORgUq8OjYS+/85HP99vB9G9VBDLpPgVGYRFm48gwFv78Csb45g57kcVPE0FxG1odLSUqSkpGDGjBlwc3MzTW+++SZSUlIAANOnT8fx48cRERGB5557Dtu3b2/RGrRaLbKysjBo0CCz+YMGDcK5c+duW8Nf/vIXlJeXIywsDE8++SQ2bNhgtd9nqx/Z6dGjB3bs2GF67eBwo6QXXngBmzdvxtq1a6FSqTB79mxMnDgRe/fuBQDo9XqMGTMGarUa+/btQ3Z2Nh5//HHI5XK8/fbbbb4tZJ/kMiniIv0RF+mPglIdNh7PxLqjV3E6U4utpzXYetp4mmt8r0BM7tcR3dRKa5dMRE0hdzEeYbHWZ9+BkpISAMDnn3+OmJgYs2W1p6T69OmD1NRUbN26FTt27MDDDz+MuLg4rFu37o4+2xKN1RAUFITk5GTs2LEDCQkJeOaZZ7B06VIkJiZCLm/ja6AJVrRw4UIhOjq63mWFhYWCXC4X1q5da5p37tw5AYCQlJQkCIIgbNmyRZBKpYJGozG1WbFihaBUKoXKysom11FUVCQAEIqKipq3ISRKZ7OKhDd+PSP0fWO7EDJ/k2kas3y3sOrPy8L10qb/jRFR6ysvLxfOnj0rlJeXW7uUZlm1apWgUqlMrwMDA4XXX3+9ye/ftm2bAEDIz88XBEEQ5HK5sG7dutu+D4CwYcOGepcFBgYKb731ltm8/v37C/Hx8U2qoa7z588LAIQjR47ctqa6GtuvTf39tvqRnYsXLyIwMBBOTk6IjY3F4sWLERwcjCNHjqCqqgpxcXGmtt26dUNwcDCSkpIwcOBAJCUloWfPnvD39ze1GTlyJGbNmoUzZ86gd+/e9X5mZWUlKisrTa+1Wm3rbSDZre4BSrwyNhLzR3dDYnIe1h25ip3nc3A6U4vTmWex5Ldk/HVAMP4+JAxqlZO1yyUikXnttdfw3HPPQaVSYdSoUaisrMThw4dx/fp1zJ07F8uWLUNAQAB69+4NqVSKtWvXQq1Ww8PDA4BxRNbOnTsxaNAgKBQKeHp6NvhZqampOH78uNm88PBwvPTSS1i4cCE6d+6MXr16YdWqVTh+/Di+/fZbAGi0htWrV0Ov1yMmJgYuLi745ptv4OzsbNavp61YNezExMRg9erViIiIQHZ2Nl577TUMGTIEp0+fhkajgaOjo2mn1fL394dGowEAaDQas6BTu7x2WUMWL16M1157rWU3hkSr7mmu66U6bDyRhe8PZeBcthb//TMVXyelYVLfDnhqaGd04h3ZiaiF/P3vf4eLiwuWLl2Kl156Ca6urujZsyfmzJkDAHB3d8eSJUtw8eJFyGQy9O/fH1u2bIFUauyO+95772Hu3Ln4/PPP0aFDB1y5cqXBz5o7d+4t8/bs2YPnnnsORUVFmDdvHnJzcxEZGYmNGzciPDz8tjV4eHjgnXfewdy5c6HX69GzZ0/8+uuv8Pb2bvF/q9uRCIIgtPmnNqCwsBAhISFYtmwZnJ2d8cQTT5gdgQGAAQMG4N5778W7776LmTNnIi0tDb/99ptpeVlZGVxdXbFlyxaMHj263s+p78hOUFAQioqKoFSyPwbdniAI2H3xGj7+4xIOphYAAKQSYGxUIGbd0xndA/h3RNTWKioqkJqaitDQUDg58WirWDS2X7VaLVQq1W1/v23qooIeHh7o2rUrLl26BLVaDZ1Oh8LCQrM2OTk5pmF0arX6ltFZta8bG2qnUCigVCrNJiJLSCQSDOvqix+fisXap2Nxb4QvDAKw8UQWRn+4BzNWH8KRtOvWLpOIiGBjYaekpAQpKSkICAhA3759IZfLsXPnTtPy5ORkpKenIzY2FgAQGxuLU6dOITc319QmISEBSqUSkZGRbV4/tU/9O3lh1RMDsPm5wRgbFQCJBNh5PheTVuzDo58lYfeFPNjQAVQionbHqn12XnzxRTz44IMICQlBVlYWFi5cCJlMhsceewwqlQozZszA3Llz4eXlBaVSiWeffRaxsbEYOHAgAGDEiBGIjIzE1KlTsWTJEmg0GrzyyiuIj4+HQqGw5qZRO9QjUIX//LUP5uaV4NPEy1h/7Cr2Xy7A/ssH0bODCvH3dsaISDWk0ju7qioREVnGqmHn6tWreOyxx5Cfnw9fX18MHjwY+/fvN1358f3334dUKsWkSZNQWVmJkSNHmt0XRCaTYdOmTZg1axZiY2NNl9B+/fXXrbVJRAjzdcO7k6Mw5/5wfL47FWsOpuFUZhGe/uYoOvu6YtY9XTCuVyDkMps6sEpEJFo21UHZWprawYmoOfJLKrF63xWs3ncFxRXGq4d28HDGU8PC8HC/IDjJm3fPGiIyV9uRtVOnTnB2drZ2OdRCysvLceXKFfF0UCYSI283BeaNiMC+f9yH+aO6wcdNgczCciz45QwGv/s7Ptl1CdqKKmuXSWT3aq8srNPprFwJtaSyMuPNXO/kqss8sgMe2aG2VVGlx9rDGViZeBmZheUAAHcnB0yL7YQnBnWCtxv7mxE1hyAISE9PR1VVFQIDA03XmyH7JAgCysrKkJubCw8PDwQEBNzSpqm/3ww7YNgh66jSG7DxeBY+2XUJKXmlAAAnuRSP9g/GzKFhCPTgYXgiS+l0OqSmpsJg4M17xaL2zu0Sya2DOxh2LMCwQ9ZkMAjYfjYHH/9xCacyiwAADlIJ/hoTjDlxXeHl6mjlConsi8Fg4KkskZDL5abTk/Vh2LEAww7ZAkEQ8Ocl41WZ9182XpVZ6eSA5+O6YurAEDg68JA8EVFdDDsWYNghW7Pv0jW8vukszmuKAQChPq741wPdMby7X72HcomI2iOGHQsw7JAt0hsE/Hg4A+9tT8a1EuMh+UFdvPHKmEjee4uICAw7FmHYIVtWXFGFj/9IwZd/pkKnN0AqAR7pH4x5I7rChyO3iKgdY9ixAMMO2YP0/DK8s+0ctpzSAADcFQ6YfV8XTB/UCQoHXpiQiNofhh0LMOyQPTmYWoDXN53B6UwtACDYywUvj+6GUXfVPzSTiEisGHYswLBD9sZgELD+WCaWbDuP3OJKAMCAUC8sGBuJuzqorFwdEVHbYNixAMMO2avSymp8mpiCT3dfRmW1ARIJMLlPR7w0MgJ+Sqfbr4CIyI4x7FiAYYfsXWZhOZZsO49fjmcBAFwcZYi/twtmDA7ljUaJSLQYdizAsENicTT9Ol7/9SyOZxQCMN5dff7obngwKoD9eYhIdBh2LMCwQ2IiCAI2nsjCu1vPI6uoAgDQN8QTr46NRK8gD+sWR0TUghh2LMCwQ2JUrtPj8z2XsWJXCsqr9ACACb074P+NikCAijcZJSL7x7BjAYYdErMcbQWWbEvGT0evAjDeWf2poZ0x657O7M9DRHaNYccCDDvUHpy8Wog3Np3FoSvXAQAh3i54a3xPDA73sXJlRETN09Tfb95GmaidiOrogR+fisXHf+0DtdIJafll+L8vDuCFH44jv6TS2uUREbUahh2idkQikWBMVAAS5g7F9Ls7QSIBNhzLxPBlifjxcAZ4oJeIxIhhh6gdcneSY9FDPbDhmUHoHqBEYVkV/t+6k3js8/24nFdi7fKIiFoUww5RO9YryAMbZw/Cy6O7wUkuxf7LBRj1wR58uOMiKqv11i6PiKhFMOwQtXNymRRPDeuMhBeGYVhXX+j0Bry/4wLGLP8TB1MLrF0eEdEdY9ghIgBAkJcLVj/RH8sf6w0fN0dcyi3Bw58m4eX1J1FUVmXt8oiImo1hh4hMJBIJHooOxM659+CxAUEAgO8OZmD4sl345XgmOzATkV1i2CGiW6hc5Fg8MQo/PhWLLn5uuFaiw/PfH8e0VYeQUVBm7fKIiCzCsENEDRoQ6oXNzw3G3Pu7wtFBit0X8nD/+4lYmZiCKr3B2uURETUJww4RNUrhIMNzw8Ox7fkhiA3zRkWVAe9sPY8HP/rTdHd1IiJbxrBDRE0S5uuGNU/GYOnkKHi4yHFeU4wJn+zFwl9Oo7iCHZiJyHYx7BBRk0kkEvylXxB2zh2Gib07QBCAr5LScP+y3dh2WmPt8oiI6sWwQ0QW83ZTYNkjvfDNjBiEeLtAo63A098cwZNfH0Z2Ubm1yyMiMsOwQ0TNNjjcB7/NGYr4ezvDQSpBwtkcxL2XiFV7U6E3cJg6EdkGhh0iuiNOchleGtkNm58bgr4hnijV6fHar2cxeeU+XMwptnZ5REQMO0TUMiLU7lj7VCzeGH8X3BQOOJZeiAeW78EHOy5AV81h6kRkPQw7RNRipFIJpg4MQcLcoRjezQ9VegEf7LiIsR/twbH069Yuj4jaKYYdImpxASpn/HdaPyx/rDe8XR1xIacEE1fsw+u/nkWZrtra5RFRO8OwQ0StovY+WzvqDFP/cm8qRry/G7sv5Fm7PCJqRxh2iKhVebo6YtkjvbD6if7o4OGMq9fL8fiXBzHvxxMoLNNZuzwiagcYdoioTdwT4YftLwzF9Ls7QSIBfjp6FXHLErH5ZDbvpk5ErYphh4jajKvCAYse6oF1T99tupt6/JqjmPm/I9AUVVi7PCISKYYdImpzfUM8sfm5wXhueDjkMuPFCO9flog1B9Jh4MUIiaiFMewQkVUoHGSYe39XbHp2CKKDPFBcWY1/bjiFxz7fj9RrpdYuj4hEhGGHiKwqQu2O9bPuxqtjI+Esl+FAagFGfbAbK3aloFrPixES0Z1j2CEiq5NJJZgxOBTbXxiKIeE+qKw24N1t5zHu4704nVlk7fKIyM4x7BCRzQjycsHXfxuAf/8lGipnOc5kaTHu4714d9t5VFTprV0eEdkphh0isikSiQST+3ZEwtyhGNMzAHqDgBW7UjD6wz04cDnf2uURkR1i2CEim+Tn7oSPp/TBZ1P7wl+pQOq1Ujzy2X78c8MpaCuqrF0eEdkRhh0ismkjeqix/YVheGxAMABgzYF0xL2XiE0ns3gxQiJqEoYdIrJ5Kmc5Fk/sie+eHIhQH1fkFldi9ppjmLbqENLyOUydiBrHsENEdiO2sze2Pj8Ec+LC4SiTYveFPIx4fzc+2nkRldXswExE9WPYISK74iSXYU5cV2ybMwSDuxiHqb+XcAGjP9yDfSnXrF0eEdkghh0iskthvm7434wB+PDRXvBxU+ByXin++vkBzP3hOK6VVFq7PCKyIQw7RGS3JBIJxvXqgJ3zhmHqwBBIJMD6Y5kY/h7vs0VEN0gEDmeAVquFSqVCUVERlEqltcshomY6nlGIf64/hbPZWgBAn2APvDm+JyID+b0mEqOm/n7zyA4RiUavIA9snD0IC8ZGwtVRhqPphXjwP3/irc1nUVpZbe3yiMhKGHaISFQcZFL8bXAods67Bw/0VENvEPD5nlTELUvEb2c0vDYPUTvEsENEoqRWOeGTKX2xanp/BHk5I7uoAk/97wie/Powrl4vs3Z5RNSGbCbsvPPOO5BIJJgzZ45pXkVFBeLj4+Ht7Q03NzdMmjQJOTk5Zu9LT0/HmDFj4OLiAj8/P7z00kuorubhaiIyurebH7bPGYb4eztDLpNgx7lc3L9sN1YmpqBKb7B2eUTUBmwi7Bw6dAiffvopoqKizOa/8MIL+PXXX7F27VokJiYiKysLEydONC3X6/UYM2YMdDod9u3bh6+++gqrV6/GggUL2noTiMiGOTvK8NLIbtjy3BAMCPVCeZUe72w9j7HL/8ShKwXWLo+IWpnVR2OVlJSgT58++OSTT/Dmm2+iV69e+OCDD1BUVARfX1+sWbMGkydPBgCcP38e3bt3R1JSEgYOHIitW7di7NixyMrKgr+/PwBg5cqVmD9/PvLy8uDo6NikGjgai6j9EAQBPx3NxNtbzqGgVAcAeKRfEP4xuhs8XZv23wwisg12MxorPj4eY8aMQVxcnNn8I0eOoKqqymx+t27dEBwcjKSkJABAUlISevbsaQo6ADBy5EhotVqcOXOmwc+srKyEVqs1m4iofZBIJJjctyN2zh2GR/sHAQB+OJyB+97bhbWHM9iBmUiErBp2vv/+exw9ehSLFy++ZZlGo4GjoyM8PDzM5vv7+0Oj0Zja1A06tctrlzVk8eLFUKlUpikoKOgOt4SI7I2nqyPemRSFdU/HIsLfHdfLqvDSupN45NP9OK/h/wARiYnVwk5GRgaef/55fPvtt3BycmrTz3755ZdRVFRkmjIyMtr084nIdvTr5IVNzw3Gy6O7wVkuw8ErBXjgwz3454ZTvO0EkUhYLewcOXIEubm56NOnDxwcHODg4IDExEQsX74cDg4O8Pf3h06nQ2Fhodn7cnJyoFarAQBqtfqW0Vm1r2vb1EehUECpVJpNRNR+yWVSPDWsMxLmDsUDPdUwCMCaA+m4d+kufLY7hXdUJ7JzVgs7w4cPx6lTp3D8+HHT1K9fP0yZMsX0XC6XY+fOnab3JCcnIz09HbGxsQCA2NhYnDp1Crm5uaY2CQkJUCqViIyMbPNtIiL71tHTBZ9M6YsfZg7EXR2UKK6sxttbzmPE+7t5QUIiO2b10Vh13XPPPabRWAAwa9YsbNmyBatXr4ZSqcSzzz4LANi3bx8A49DzXr16ITAwEEuWLIFGo8HUqVPx97//HW+//XaTP5ejsYjoZgaDgJ+OXsWS35KRV2w8nRUb5o1Xx0byXltENsJuRmM15v3338fYsWMxadIkDB06FGq1GuvXrzctl8lk2LRpE2QyGWJjY/F///d/ePzxx/H6669bsWoiEgOpVIK/9AvCHy/eg9n3doGjgxRJl/Mx5qM9eHn9SVMAIiLbZ1NHdqyFR3aI6HYyCsrw7rbz2HQyGwDgpnDA7Pu64IlBnaBwkFm5OqL2qam/3ww7YNghoqY7dKUAb2w6i5NXiwAAwV4u+OcD3TCyhxoSicTK1RG1Lww7FmDYISJLGAwCNhzLxLvbziO35nRWTKgXXh0bibs6qKxcHVH7wbBjAYYdImqO0spqfJqYgk93X0ZltQESCfBw3yDMG9kVfu5te/0wovaIYccCDDtEdCcyC8vx7tbz2HgiCwDg6ihD/H1d8LdBoXCSsz8PUWth2LEAww4RtYQjaQV4fdM5nMgoBAB09HTGPx/ojtF3sT8PUWtg2LEAww4RtRSDQcAvJzLx7tZkaLQVAIABnYz9eXp2ZH8eopbEsGMBhh0iamllump8mngZn+5OQUWVsT/P5D4d8dLICPgp2Z+HqCUw7FiAYYeIWktWYTmWbDuPn48b+/O4OMrwxKBOmDmkM1QucitXR2TfGHYswLBDRK3taPp1vLHpLI6lFwIA3J0cMHNIGJ4YHAo3hYN1iyOyUww7FmDYIaK2IAgCtp/NwbLtF5CcUwwA8HSRY9Y9nfF4bCeO3CKyEMOOBRh2iKgtGQwCfj2ZhQ92XETqtVIAgJ+7ArPv64JH+gfx9hNETcSwYwGGHSKyhmq9AeuPZeLDHReRWVgOAOjg4Yznh4djYp8OcJDZ9L2aiayOYccCDDtEZE2V1Xr8eCgDH/1+yXT7iVAfV8yJC8eDUYGQSnmNHqL6MOxYgGGHiGxBuU6Pb/anYUViCgpKdQCACH93zB3RFSMi/XlhQqKbMOxYgGGHiGxJSWU1Vv2Zis/2XEZxRTUAIKqjCnPv74phXX0ZeohqMOxYgGGHiGxRUVkVPtuTglV7r6BMpwcA9O/kiXkjIjAwzNvK1RFZH8OOBRh2iMiWXSupxMpdKfh6fxp01QYAwJBwH8y9vyt6B3tauToi62HYsQDDDhHZA01RBf7zx0V8fzAD1Qbjf7rjuvth7v0RiAzkf7uo/WHYsQDDDhHZk4yCMny48yLWH72KmsyDsVEBmBPXFV383KxbHFEbYtixAMMOEdmjlLwSvJ9wAZtOZgMApBJgfK8OePqezujq727l6ohaH8OOBRh2iMiencvW4r3tF7DjXI5pXlx3Pzw1rDP6d/KyYmVErYthxwIMO0QkBievFmLFrhRsO6NB7X/Z+4V44ulhnXFfNz9enJBEh2HHAgw7RCQmKXkl+Hz3Zaw/mgmd3jh6K9zPDU8N64yHogPh6MDbUJA4MOxYgGGHiMQoV1uBL/amYs3+dBRXGi9OGKBywozBoXhsQDBcFQ5WrpDozjDsWIBhh4jETFtRhW/3p+PLvanIq7n3lspZjsdjQzD97k7wdlNYuUKi5mHYsQDDDhG1BxVVemw4lolPE1NwJb8MAKBwkOKR/kF4ckgYgrxcrFwhkWUYdizAsENE7YneIOC3MxqsTEzByatFAACZVIIxPQPw9LDOvEAh2Q2GHQsw7BBReyQIApJS8rEiMQV7Ll4zzR/a1RdPDwtDbJg3bzpKNo1hxwIMO0TU3p3OLMKnuy9j88ks01WZozuq8PSwzhjRQw0Zh62TDWLYsQDDDhGRUXp+GT7fcxk/Hs5AZc1NR8N8XPHk0DBM7NMBCgeZlSskuoFhxwIMO0RE5q6VVOKrfVfwdVIaisqrAAC+7gpMiw3BowOC4cMRXGQDGHYswLBDRFS/kspqfH8wHf/dkwqNtgIA4CiT4oGeajx+dyf0DvJgvx6yGoYdCzDsEBE1TldtwKaTWfgqKQ0nMgpN8+/qoMTjsZ3wUHQgnOQ8xUVti2HHAgw7RERNdyKjEF8npeHXk1nQ1fTr8XCR45F+Qfi/gSG8Xg+1GYYdCzDsEBFZrqBUhx8OZeCb/WnILCwHAEgkwH0RfpgaG4Kh4b68+Si1KoYdCzDsEBE1n94g4Pfzufg66YrZ9Xo6ebvg/waG4C/9gqBylluxQhIrhh0LMOwQEbWMy3kl+N/+NKw7fNV081FnuQzjewdi6sBOvDoztSiGHQsw7BARtazSymr8fDwT/0tKw3lNsWl+/06eeDy2E0b2UMPRQWrFCkkMGHYswLBDRNQ6BEHAwdQCfJ2Uhm1nNNDXXJ7Z112Bvw4Ixl9jguGvdLJylWSvGHYswLBDRNT6NEUVWHMwHd8dTEdecSUAwEEqwci71Hh8YAgGhHrxmj1kEYYdCzDsEBG1HV21Ab+d0eDrpCs4dOW6aX43tTse6R+Ecb06wMvV0YoVkr1g2LEAww4RkXWczdLif/uvYMOxTFRUGa/ZI5dJcF83P0zuG4R7Inwhl7FvD9WPYccCDDtERNZVVFaFn49nYt2RqziVWWSa7+3qiPG9O2By347oHsD/PpM5hh0LMOwQEdmO8xotfjpyFRuOZeJaic40/64OSkzu0xEP8TQX1WDYsQDDDhGR7anSG7D7Qh7WHr6KnedzUKU3/lzJZRIM7+aPyX07YhhPc7VrDDsWYNghIrJtBaU6bDyeiXVHr+J0ptY038fNEeN7dcDkfh3RTc3/frc3DDsWYNghIrIf57KNp7l+Pm5+mqtnBxUm9+2Ih6ID4cnTXO0Cw44FGHaIiOxPld6AxOQ8rDty62muuO41p7m6+sKBp7lEi2HHAgw7RET2raBUh19qRnOdyap7mkuBCb0DMblvECLU7laskFoDw44FGHaIiMTjbJYWPx29ip+PZSK/1Hw010PRgRgTFYgOHs5WrJBaCsOOBRh2iIjEp0pvwK7kPKw7koGd53JRbbjxc9cn2ANjowIxJiqA9+ayYww7FmDYISISt/ySSmw5rcGmE1k4eKUAtb98EgkwoJMXxkYHYvRdavi4KaxbKFmEYccCDDtERO1HjrYCW05l49cTWTiaXmiaL5NKcHdnb4yNCsDIHmp4uHBEl61j2LEAww4RUfuUWViOzSezsOlkNk5evXGbCrlMgsFdfPBgdCDuj/SHu5PcilVSQxh2LMCwQ0REV66VYnPNEZ/zmmLTfEcHKe7p6osHowMxvLsfXBwdrFgl1dWqYScjIwMSiQQdO3YEABw8eBBr1qxBZGQkZs6c2fyqrYRhh4iI6rqUW4xfT2Rj08kspOSVmuY7y2W4r7sfHowKwD0RfnCSy6xYJbVq2BkyZAhmzpyJqVOnQqPRICIiAj169MDFixfx7LPPYsGCBXdUfFtj2CEiovoIgoDzmmJsqjnVlZZfZlrmpnDA/ZH+GBsVgCHhvnB04MUL21qrhh1PT0/s378fERERWL58OX744Qfs3bsX27dvx9NPP43Lly/fUfFtjWGHiIhuRxAEnMoswqaT2dh8MhuZheWmZUonB9wfqcaIHv4YGu4LZ0ce8WkLTf39blYMraqqgkJhHJ63Y8cOPPTQQwCAbt26ITs7u8nrWbFiBaKioqBUKqFUKhEbG4utW7ealldUVCA+Ph7e3t5wc3PDpEmTkJOTY7aO9PR0jBkzBi4uLvDz88NLL72E6urq5mwWERFRgyQSCaI6euCfD3THnv93L36adTem390Jfu4KaCuq8dPRq3jqf0fQ6/Xt+PtXh/DjoQxcK6m0dtmEZh7ZiYmJwb333osxY8ZgxIgR2L9/P6Kjo7F//35MnjwZV69ebdJ6fv31V8hkMoSHh0MQBHz11VdYunQpjh07hh49emDWrFnYvHkzVq9eDZVKhdmzZ0MqlWLv3r0AAL1ej169ekGtVmPp0qXIzs7G448/jieffBJvv/12k7eHR3aIiKi59AYBh64UYPuZHCSc0yCj4MYRH4kE6BvsiRE9/HF/pBqhPq5WrFR8WvU01q5duzBhwgRotVpMmzYNX375JQDgn//8J86fP4/169c3u3AvLy8sXboUkydPhq+vL9asWYPJkycDAM6fP4/u3bsjKSkJAwcOxNatWzF27FhkZWXB398fALBy5UrMnz8feXl5cHSs/xoJlZWVqKy8kba1Wi2CgoIYdoiI6I7U9vFJOJuDhLM5OJVZZLa8i58bRkT64/5If0R39IBUKrFSpeLQ6kPP9Xo9tFotPD09TfOuXLliOp3UnPWtXbsW06ZNw7Fjx6DRaDB8+HBcv34dHh4epnYhISGYM2cOXnjhBSxYsAAbN27E8ePHTctTU1MRFhaGo0ePonfv3vV+1qJFi/Daa6/dMp9hh4iIWlJWYTl2nDMGn6SUfLNbVvi5KzC8uz9G9PDH3Z29oXBgPx9LNTXsNOtiAeXl5RAEwRR00tLSsGHDBnTv3h0jR460aF2nTp1CbGwsKioq4Obmhg0bNiAyMhLHjx+Ho6OjWdABAH9/f2g0GgCARqMxHdGpu7x2WUNefvllzJ071/S69sgOERFRSwr0cMbjsZ3weGwnFJVXYVdyLhLO5mBXch5yiyvx3cF0fHcwHa6OMgyL8MWISDXujfCDyoUXMWxJzQo748aNw8SJE/H000+jsLAQMTExkMvluHbtGpYtW4ZZs2Y1eV0RERE4fvw4ioqKsG7dOkybNg2JiYnNKavJFAqFqYM1ERFRW1A5yzGuVweM69UBldV67L9cgISzGiSczUGOthJbTmmw5ZQGDlIJBoR6GU939VDzDu0toFmjsY4ePYohQ4YAANatWwd/f3+kpaXh66+/xvLlyy1al6OjI7p06YK+ffti8eLFiI6Oxocffgi1Wg2dTofCwkKz9jk5OVCr1QAAtVp9y+is2te1bYiIiGyNwkGGYV198eb4nkj6x3D8Ej8Is+/tggh/d1QbBOxLyceiX89i0Du/44EP9+D9hAs4ebUQBkO7v+lBszTryE5ZWRnc3d0BANu3b8fEiRMhlUoxcOBApKWl3VFBBoMBlZWV6Nu3L+RyOXbu3IlJkyYBAJKTk5Geno7Y2FgAQGxsLN566y3k5uaa+gklJCRAqVQiMjLyjuogIiJqC1KpBNFBHogO8sCLIyOQll+KhLM52H42B4evFOBsthZns7X4cOdFeLk6Yki4D4aG+2JIVx/4uTtZu3y70Kyw06VLF/z888+YMGECfvvtN7zwwgsAgNzcXIs6+L788ssYPXo0goODUVxcjDVr1mDXrl347bffoFKpMGPGDMydOxdeXl5QKpV49tlnERsbi4EDBwIARowYgcjISEydOhVLliyBRqPBK6+8gvj4eJ6mIiIiuxTi7Yq/DwnD34eEoaBUh53ncrDjXA72XcpHQakOvxzPwi/HswAAPQKVGNrVF8O6+qJPsCev4tyAZo3GWrduHf76179Cr9fjvvvuQ0JCAgBg8eLF2L17t9mFARszY8YM7Ny5E9nZ2VCpVIiKisL8+fNx//33AzBeVHDevHn47rvvUFlZiZEjR+KTTz4xO0WVlpaGWbNmYdeuXXB1dcW0adPwzjvvwMGh6TmO19khIiJbV6U34Fh6IRIv5GL3hWu3DGt3dZTh7i4+GFYTfoK8XKxUadtp9aHnGo0G2dnZiI6OhlRqTJIHDx6EUqlEt27dmle1lTDsEBGRvblWUok9F/Ow+8I17L6Qh/xSndnyMB9X01GfmDAvUd6tvdXDTq3aqyXX3gHdHjHsEBGRPTMYBJzJ0mL3xTwkJufhSPp16Ot0ZnaUSTEg1AvDuvpiaFdfdPV3g0Ri/xc0bNWwYzAY8Oabb+K9995DSUkJAMDd3R3z5s3Dv/71L9ORHnvBsENERGKirajCvkv5SLyQh90X8sxuWgoAaqUThnb1wbCufhjcxcdur+vTqmHn5ZdfxhdffIHXXnsNgwYNAgD8+eefWLRoEZ588km89dZbza/cChh2iIhIrARBQEpeqSn47L+cj8pqg2m5VAJEB3lgUGcfxHb2Rt8QTzjJ7eNqzq0adgIDA7Fy5UrT3c5r/fLLL3jmmWeQmZlpecVWxLBDRETtRUWVHgdTC5B4IQ+JF/JwKbfEbLmjTIrewR6I7eyN2DBv9Ar2sNlbWbRq2HFycsLJkyfRtWtXs/nJycno1asXysvLG3inbWLYISKi9iqzsBx/XsxDUko+ki7nI0dbabbcSS5FvxAvxHb2xsAwb0R1VEEus43uKq0admJiYhATE3PL1ZKfffZZHDx4EAcOHLC8Yiti2CEiIjKe8kq9Vop9NcFnf0r+LaO8XB1l6NfJC3d39kZsZ2/0CFRBZqW7t7dq2ElMTMSYMWMQHBxsuppxUlISMjIysGXLFtOtJOwFww4REdGtBEHAxdwSJKXkY1/KNRxILUBhWZVZG3cnB8SEeiG2sw9iw7zRTe0OaRuFn1Yfep6VlYWPP/4Y58+fBwB0794dM2fOxJtvvonPPvuseVVbCcMOERHR7RkMAs5ptEhKycf+y/k4cLkAxZXVZm08XeSICfXG3V2MfX66+LXeMPc2u85OXSdOnECfPn2g1+tbapVtgmGHiIjIctV6A85kaZF0OR9JKfk4dKUAZTrzDODjpsDAMC/MieuKLn5uLfr5Tf39Ft/lFImIiKhNOMikppuYPj2sM6r0Bpy8WoSklGtIupyPw1eu41pJJTadzMZLIyOsV6fVPpmIiIhERS6Tom+IJ/qGeGL2feGorNbjeHohjmcUItiK9+pi2CEiIqJWoXCQISbMGzFh3latw6KwM3HixEaXFxYW3kktRERERC3OorCjUqluu/zxxx+/o4KIiIiIWpJFYWfVqlWtVQcRERFRq7CN6z0TERERtRKGHSIiIhI1hh0iIiISNYYdIiIiEjWGHSIiIhI1hh0iIiISNYYdIiIiEjWGHSIiIhI1hh0iIiISNYYdIiIiEjWGHSIiIhI1hh0iIiISNYYdIiIiEjWGHSIiIhI1hh0iIiISNYYdIiIiEjWGHSIiIhI1hh0iIiISNYYdIiIiEjWGHSIiIhI1hh0iIiISNYYdIiIiEjWGHSIiIhI1hh0iIiISNYYdIiIiEjWGHSIiIhI1hh0iIiISNYYdIiIiEjWGHSIiIhI1hh0iIiISNYYdIiIiEjWGHSIiIhI1hh0iIiISNYYdIiIiEjWGHSIiIhI1hh0iIiISNYYdIiIiEjWGHSIiIhI1hh0iIiISNYYdIiIiEjWGHSIiIhI1hh0iIiISNauGncWLF6N///5wd3eHn58fxo8fj+TkZLM2FRUViI+Ph7e3N9zc3DBp0iTk5OSYtUlPT8eYMWPg4uICPz8/vPTSS6iurm7LTSEiIiIbZdWwk5iYiPj4eOzfvx8JCQmoqqrCiBEjUFpaamrzwgsv4Ndff8XatWuRmJiIrKwsTJw40bRcr9djzJgx0Ol02LdvH7766iusXr0aCxYssMYmERERkY2RCIIgWLuIWnl5efDz80NiYiKGDh2KoqIi+Pr6Ys2aNZg8eTIA4Pz58+jevTuSkpIwcOBAbN26FWPHjkVWVhb8/f0BACtXrsT8+fORl5cHR0fHWz6nsrISlZWVptdarRZBQUEoKiqCUqlsm40lIiKiO6LVaqFSqW77+21TfXaKiooAAF5eXgCAI0eOoKqqCnFxcaY23bp1Q3BwMJKSkgAASUlJ6NmzpynoAMDIkSOh1Wpx5syZej9n8eLFUKlUpikoKKi1NomIiIiszGbCjsFgwJw5czBo0CDcddddAACNRgNHR0d4eHiYtfX394dGozG1qRt0apfXLqvPyy+/jKKiItOUkZHRwltDREREtsLB2gXUio+Px+nTp/Hnn3+2+mcpFAooFIpW/xwiIiKyPps4sjN79mxs2rQJf/zxBzp27Giar1arodPpUFhYaNY+JycHarXa1Obm0Vm1r2vbEBERUftl1bAjCAJmz56NDRs24Pfff0doaKjZ8r59+0Iul2Pnzp2mecnJyUhPT0dsbCwAIDY2FqdOnUJubq6pTUJCApRKJSIjI9tmQ4iIiMhmWfU0Vnx8PNasWYNffvkF7u7upj42KpUKzs7OUKlUmDFjBubOnQsvLy8olUo8++yziI2NxcCBAwEAI0aMQGRkJKZOnYolS5ZAo9HglVdeQXx8PE9VERERkXWHnkskknrnr1q1CtOnTwdgvKjgvHnz8N1336GyshIjR47EJ598YnaKKi0tDbNmzcKuXbvg6uqKadOm4Z133oGDQ9OyXFOHrhEREZHtaOrvt01dZ8daGHaIiIjsj11eZ4eIiIiopTHsEBERkagx7BAREZGoMewQERGRqDHsEBERkagx7BAREZGoMewQERGRqDHsEBERkagx7BAREZGoMewQERGRqDHsEBERkagx7BAREZGoMewQERGRqDHsEBERkagx7BAREZGoMewQERGRqDHsEBERkagx7BAREZGoMewQERGRqDHsEBERkagx7BAREZGoMewQERGRqDHsEBERkagx7BAREZGoMewQERGRqDHsEBERkagx7BAREZGoMewQERGRqDHsEBERkagx7BAREZGoMewQERGRqDHsEBERkagx7BAREZGoMewQERGRqDHsEBERkagx7BAREZGoMewQERGRqDHsEBERkagx7BAREZGoMewQERGRqDHsEBERkagx7BAREZGoMewQERGRqDHsEBERkagx7BAREZGoMewQERGRqDHsEBERkagx7BAREZGoMewQERGRqDHsEBERkagx7BAREZGoMewQERGRqDHsEBERkagx7BAREZGoMewQERGRqDHsEBERkagx7BAREZGoWTXs7N69Gw8++CACAwMhkUjw888/my0XBAELFixAQEAAnJ2dERcXh4sXL5q1KSgowJQpU6BUKuHh4YEZM2agpKSkDbeCiIiIbJlVw05paSmio6Px8ccf17t8yZIlWL58OVauXIkDBw7A1dUVI0eOREVFhanNlClTcObMGSQkJGDTpk3YvXs3Zs6c2VabQERERDZOIgiCYO0iAEAikWDDhg0YP348AONRncDAQMybNw8vvvgiAKCoqAj+/v5YvXo1Hn30UZw7dw6RkZE4dOgQ+vXrBwDYtm0bHnjgAVy9ehWBgYH1flZlZSUqKytNr7VaLYKCglBUVASlUtm6G0pEREQtQqvVQqVS3fb322b77KSmpkKj0SAuLs40T6VSISYmBklJSQCApKQkeHh4mIIOAMTFxUEqleLAgQMNrnvx4sVQqVSmKSgoqPU2hIiIiKzKZsOORqMBAPj7+5vN9/f3Ny3TaDTw8/MzW+7g4AAvLy9Tm/q8/PLLKCoqMk0ZGRktXD0RERHZCgdrF2ANCoUCCoXC2mUQERFRG7DZIztqtRoAkJOTYzY/JyfHtEytViM3N9dseXV1NQoKCkxtiIiIqH2z2bATGhoKtVqNnTt3muZptVocOHAAsbGxAIDY2FgUFhbiyJEjpja///47DAYDYmJi2rxmIiIisj1WPY1VUlKCS5cumV6npqbi+PHj8PLyQnBwMObMmYM333wT4eHhCA0NxauvvorAwEDTiK3u3btj1KhRePLJJ7Fy5UpUVVVh9uzZePTRRxscidWmvnoQuJ4GyOSAzNH4KK3z3KL5DjWPjoC0znOZHHBwAhwcax6djPPrzjO9VgBSmbX/VYiIiNqUVcPO4cOHce+995pez507FwAwbdo0rF69Gv/v//0/lJaWYubMmSgsLMTgwYOxbds2ODk5md7z7bffYvbs2Rg+fDikUikmTZqE5cuXt/m21KvoKlCYZu0qzEkdAJnCGHxM002ByEFxo43cBZA7GydH15rnLjfmm+a51t+O4YqIiKzMZq6zY01NHadvsZyzQFUZoNfVTNU3nhvqPNdX1Uw6wFDnee18Q9Wt7eo+VlcA+kqguu5UYVxeXQEIhpbbJkvJFI0HJYV7zaRs4PlN8xwcrbctRERkU5r6+90uR2O1Gf9Ia1dgpK82Dz+1gUhfJxhV6+oPTVVlQFV5zWPNc12p+TzdzW3K6nx2zedUFLbMtjg41R+C6pvnpAKcPQFnL8DZw/jcyQOQ2mxXNSIiagUMO+2BzAGQubXd5wlCTfi5KQCZQlFNWNKVAJUlQGVxnUlb/+vaAFVdYZxK85pZnKQm+HjVBCFPwKXOc7P5njeeK1QMSUREdophh1qeRAI4uhgneLfMOvXVgK74piBUTziq0N6YX1EElF+/MelKAAg3Xlu0TVLjUaG6wcjFB3DzBVx9AVe/mud+gJsf4OLN/kpERDaCYYfsg8zhRshormpdnfBTcON5WcGt88vqhKSqUmO/p/IC49QkEmPgcfOrCUO+N567+RlDkavPjXkOvMglEVFrYdih9sPBEXD3N06WqKow9jm6ORSVXjOeTivJBUpzgZI84+uyfAACUHbNODWFk+rWI0TuasA9wPioDDQ+OnkYj5wREVGTMewQ3Y7cCZCrjWGjKfTVxsBTmlsThvJqwlBuTUCqfV4TjgzVxlNuFUVA/qXG1+3gVBOCAm+EIWXAjVBU++joeufbTUQkEgw7RC1N5tD0I0gGg/GoUd0jRKXXgJIcoDgHKM4CijVAcbbxiFJ1BXD9inFqjEJVE37qHBVyD6gz1SyTyVtgg4mIbBvDDpE1SaXGTs8uXoBvRONtqyqMoac2/JgmDaCt87qqDKgsMk7XkhtZoQRw8zeGIWUgoOxgfFR1vDHPPYD9iYjI7jHsENkLuRPgFWqcGiIIxpFopkCkAbRZNwWkmueGaqBEY5yyjja8Tldf8zBk9ryDMRA5urT89hIRtRCGHSIxkUiMnZ2dVI0fKTIYjKfOtJnGMKTNqv+5vvJG36LsEw2vz9nTPAy5B9YJRjWTQsnO1URkFQw7RO2RVHqjX1GHPvW3EQTjCLTiRsJQUaZxaH7tKLWc0w1/pqNbTYfqm0JQ3WDk4sOLNxJRi2PYIaL6SSSAq7dxUvesv03tabObA5ApIGUb51cUGi/qmH/RODVEKq8ZXVbPkaHaeexYTUQWYtghouare9rMr3vD7XRlxn5C2swbAag4u05IyjaOQDNUAYXpxqnhDzX2I6o71N401RmC7+rLq1gTEQCGHSJqC44ugHdn49QQfZUx8NQNQPWFIkNVzRD9XEBzsuH1SaS3XpzR7NHf+MhTZ0Six7BDRLZBJjcOe1d1bLiNwWC8YGPtqLISjfnIM9P8XEDQ3xhtln284XVKHYxD8GtDkJt/zVRznzM3/xu3+ZA7t/hmE1HrY9ghIvshlRpvp+HmCwRENdzOoDdenLFuCCrJqT8UGaprjhpl3v7zFcob9zZzqzO51oQiN98b4YjXJyKyGQw7RCQ+UlnTrmKtrzYOq785BNXe0sM05RiH4VdqjdPtbusBGO9jZnZkqM7RIhcf441gXbyNyxxdOSyfqBUx7BBR+yVzMI7+UgY03q521Fnd8GO6AWyO8f5ndecZqo0j0CoKgWsXbl+Hg1NNAPKuefStE4ZqXtddrnBnOCKyAMMOEdHt1B115hPeeNva+53VhqLS2iCUc+OmsKXXjFPZNeP9zqorAO1V49QUMkUjYcgbcPYyPrp4GZ87ewIOjnf8z0Bkrxh2iIhaUt37nfl1a7ytIAC6UmPoqRuA6j6anucbg1N1ufGUWlP7GdVydAdcPGuCkNeNR1M4qglFdZc5uvEIEokCww4RkbVIJIDCzTh5dmrae3SltwagusGovMB45euyfOPz8kIAAqArNk6NXsPoJjJHYwAyBSMvY18kZw/j/NrnZo+exiNgvMYR2RCGHSIie+Loapw8Q5rW3qAHKoqMAag2CDX0WPe5vhLQ626cgrOUQgU4q5oQjm5aplAyKFGLY9ghIhIzqezGUZmmEgSgqqyBYFRo7JNU97H8+o3nVaXGdVQWGSdYcCSplqN7TR8ppTH81D53UtXzWnXrcrkzT7+RGYYdIiIyJ5HcOILkEWTZe6t1xiNJdQNQfaHI7PG68Xl1uXEdtafctM2sXypvICipjCPZbpmU5q+dlMbAJeNPpFhwTxIRUctxcLxx4UdLVVcCFVpjWKosqvO85rHB10U1j8WAYDDeUqQs3zjdCbnL7YNR3fmONf2vHF2NYcn03I2n5qyMYYeIiGyDg6L5QQmoGd1W0kAwqpl0JcZQZDZpzV9XVxjXV1VmnJrTZ+lmcpcbwUfhZnxs8LmrMUDV97x2kjnyVJ0FGHaIiEgcJJIbR1pUd7Ceal1NKNLePhiZzSupeV/No67EeIFJ4EZwKs1rkU2FRFYTgGpClNzlpte1wahmvilsuTbS3gVwcBbljXEZdoiIiOpycAQcLOzUXR9BMJ6a05Ua+yDVDUGm56XGoGSad3Pb0prnxYCuzDhKDjDe6NbUCbyFOTgbO3nLXYwBSO5sDENy55rXdSbT8vrm1b6n5tHVz2r9oBh2iIiIWoNEAsidjJOrd8usU19tHPGmKzWGH12J8YiRrvTGVFUzX1czvyntq8pufEZ1uXEqL2iZmms9sx/w696y62wihh0iIiJ7IXMAZDXD7VuSwWAMOFXlNQGo3BiSqiyYp6sJTVXlNwKUrs5ruUvL1mwBhh0iIqL2Tiq90Z/H1ad1PkMQWme9TSC+XkhERERke6w4eoxhh4iIiESNYYeIiIhEjWGHiIiIRI1hh4iIiESNYYeIiIhEjWGHiIiIRI1hh4iIiESNYYeIiIhEjWGHiIiIRI1hh4iIiESNYYeIiIhEjWGHiIiIRI1hh4iIiETNwdoF2AKh5rbzWq3WypUQERFRU9X+btf+jjeEYQdAcXExACAoKMjKlRAREZGliouLoVKpGlwuEW4Xh9oBg8GArKwsuLu7QyKRtNh6tVotgoKCkJGRAaVS2WLrtSVi30Zun/0T+zZy++yf2LexNbdPEAQUFxcjMDAQUmnDPXN4ZAeAVCpFx44dW239SqVSlH/AdYl9G7l99k/s28jts39i38bW2r7GjujUYgdlIiIiEjWGHSIiIhI1hp1WpFAosHDhQigUCmuX0mrEvo3cPvsn9m3k9tk/sW+jLWwfOygTERGRqPHIDhEREYkaww4RERGJGsMOERERiRrDDhEREYkaw84d+vjjj9GpUyc4OTkhJiYGBw8ebLT92rVr0a1bNzg5OaFnz57YsmVLG1VqucWLF6N///5wd3eHn58fxo8fj+Tk5Ebfs3r1akgkErPJycmpjSq2zKJFi26ptVu3bo2+x572X6dOnW7ZPolEgvj4+Hrb28O+2717Nx588EEEBgZCIpHg559/NlsuCAIWLFiAgIAAODs7Iy4uDhcvXrztei39HreWxravqqoK8+fPR8+ePeHq6orAwEA8/vjjyMrKanSdzfk7b02324fTp0+/pd5Ro0bddr32sA8B1PudlEgkWLp0aYPrtKV92JTfhYqKCsTHx8Pb2xtubm6YNGkScnJyGl1vc7+7TcWwcwd++OEHzJ07FwsXLsTRo0cRHR2NkSNHIjc3t972+/btw2OPPYYZM2bg2LFjGD9+PMaPH4/Tp0+3ceVNk5iYiPj4eOzfvx8JCQmoqqrCiBEjUFpa2uj7lEolsrOzTVNaWlobVWy5Hj16mNX6559/NtjW3vbfoUOHzLYtISEBAPCXv/ylwffY+r4rLS1FdHQ0Pv7443qXL1myBMuXL8fKlStx4MABuLq6YuTIkaioqGhwnZZ+j1tTY9tXVlaGo0eP4tVXX8XRo0exfv16JCcn46GHHrrtei35O29tt9uHADBq1Cizer/77rtG12kv+xCA2XZlZ2fjyy+/hEQiwaRJkxpdr63sw6b8Lrzwwgv49ddfsXbtWiQmJiIrKwsTJ05sdL3N+e5aRKBmGzBggBAfH296rdfrhcDAQGHx4sX1tn/44YeFMWPGmM2LiYkRnnrqqVats6Xk5uYKAITExMQG26xatUpQqVRtV9QdWLhwoRAdHd3k9va+/55//nmhc+fOgsFgqHe5Pe07QRAEAMKGDRtMrw0Gg6BWq4WlS5ea5hUWFgoKhUL47rvvGlyPpd/jtnLz9tXn4MGDAgAhLS2twTaW/p23pfq2cdq0acK4ceMsWo8978Nx48YJ9913X6NtbHkf3vy7UFhYKMjlcmHt2rWmNufOnRMACElJSfWuo7nfXUvwyE4z6XQ6HDlyBHFxcaZ5UqkUcXFxSEpKqvc9SUlJZu0BYOTIkQ22tzVFRUUAAC8vr0bblZSUICQkBEFBQRg3bhzOnDnTFuU1y8WLFxEYGIiwsDBMmTIF6enpDba15/2n0+nwzTff4G9/+1ujN7u1p313s9TUVGg0GrN9pFKpEBMT0+A+as732JYUFRVBIpHAw8Oj0XaW/J3bgl27dsHPzw8RERGYNWsW8vPzG2xrz/swJycHmzdvxowZM27b1lb34c2/C0eOHEFVVZXZ/ujWrRuCg4Mb3B/N+e5aimGnma5duwa9Xg9/f3+z+f7+/tBoNPW+R6PRWNTelhgMBsyZMweDBg3CXXfd1WC7iIgIfPnll/jll1/wzTffwGAw4O6778bVq1fbsNqmiYmJwerVq7Ft2zasWLECqampGDJkCIqLi+ttb8/77+eff0ZhYSGmT5/eYBt72nf1qd0Pluyj5nyPbUVFRQXmz5+Pxx57rNGbK1r6d25to0aNwtdff42dO3fi3XffRWJiIkaPHg29Xl9ve3veh1999RXc3d1ve4rHVvdhfb8LGo0Gjo6OtwTw2/021rZp6nssxbueU5PEx8fj9OnTtz1PHBsbi9jYWNPru+++G927d8enn36KN954o7XLtMjo0aNNz6OiohATE4OQkBD8+OOPTfo/LXvyxRdfYPTo0QgMDGywjT3tu/auqqoKDz/8MARBwIoVKxpta29/548++qjpec+ePREVFYXOnTtj165dGD58uBUra3lffvklpkyZctuBALa6D5v6u2ALeGSnmXx8fCCTyW7pYZ6TkwO1Wl3ve9RqtUXtbcXs2bOxadMm/PHHH+jYsaNF75XL5ejduzcuXbrUStW1HA8PD3Tt2rXBWu11/6WlpWHHjh34+9//btH77GnfATDtB0v2UXO+x9ZWG3TS0tKQkJDQ6FGd+tzu79zWhIWFwcfHp8F67XEfAsCePXuQnJxs8fcSsI192NDvglqthk6nQ2FhoVn72/021rZp6nssxbDTTI6Ojujbty927txpmmcwGLBz506z/zuuKzY21qw9ACQkJDTY3toEQcDs2bOxYcMG/P777wgNDbV4HXq9HqdOnUJAQEArVNiySkpKkJKS0mCt9rb/aq1atQp+fn4YM2aMRe+zp30HAKGhoVCr1Wb7SKvV4sCBAw3uo+Z8j62pNuhcvHgRO3bsgLe3t8XruN3fua25evUq8vPzG6zX3vZhrS+++AJ9+/ZFdHS0xe+15j683e9C3759IZfLzfZHcnIy0tPTG9wfzfnuNqdwaqbvv/9eUCgUwurVq4WzZ88KM2fOFDw8PASNRiMIgiBMnTpV+Mc//mFqv3fvXsHBwUH497//LZw7d05YuHChIJfLhVOnTllrExo1a9YsQaVSCbt27RKys7NNU1lZmanNzdv42muvCb/99puQkpIiHDlyRHj00UcFJycn4cyZM9bYhEbNmzdP2LVrl5Camirs3btXiIuLE3x8fITc3FxBEOx//wmCcVRKcHCwMH/+/FuW2eO+Ky4uFo4dOyYcO3ZMACAsW7ZMOHbsmGk00jvvvCN4eHgIv/zyi3Dy5Elh3LhxQmhoqFBeXm5ax3333Sd89NFHpte3+x7byvbpdDrhoYceEjp27CgcP37c7DtZWVnZ4Pbd7u+8rTW2jcXFxcKLL74oJCUlCampqcKOHTuEPn36COHh4UJFRYVpHfa6D2sVFRUJLi4uwooVK+pdhy3vw6b8Ljz99NNCcHCw8PvvvwuHDx8WYmNjhdjYWLP1RERECOvXrze9bsp3904w7Nyhjz76SAgODhYcHR2FAQMGCPv37zctGzZsmDBt2jSz9j/++KPQtWtXwdHRUejRo4ewefPmNq646QDUO61atcrU5uZtnDNnjunfw9/fX3jggQeEo0ePtn3xTfDII48IAQEBgqOjo9ChQwfhkUceES5dumRabu/7TxAE4bfffhMACMnJybcss8d998cff9T7N1m7HQaDQXj11VcFf39/QaFQCMOHD79l20NCQoSFCxeazWvse9yWGtu+1NTUBr+Tf/zxh2kdN2/f7f7O21pj21hWViaMGDFC8PX1FeRyuRASEiI8+eSTt4QWe92HtT799FPB2dlZKCwsrHcdtrwPm/K7UF5eLjzzzDOCp6en4OLiIkyYMEHIzs6+ZT1139OU7+6dkNR8KBEREZEosc8OERERiRrDDhEREYkaww4RERGJGsMOERERiRrDDhEREYkaww4RERGJGsMOERERiRrDDhEREYkaww4RUT0kEgl+/vlna5dBRC2AYYeIbM706dMhkUhumUaNGmXt0ojIDjlYuwAiovqMGjUKq1atMpunUCisVA0R2TMe2SEim6RQKKBWq80mT09PAMZTTCtWrMDo0aPh7OyMsLAwrFu3zuz9p06dwn333QdnZ2d4e3tj5syZKCkpMWvz5ZdfokePHlAoFAgICMDs2bPNll+7dg0TJkyAi4sLwsPDsXHjxtbdaCJqFQw7RGSXXn31VUyaNAknTpzAlClT8Oijj+LcuXMAgNLSUowcORKenp44dOgQ1q5dix07dpiFmRUrViA+Ph4zZ87EqVOnsHHjRnTp0sXsM1577TU8/PDDOHnyJB544AFMmTIFBQUFbbqdRNQCWuz+6URELWTatGmCTCYTXF1dzaa33npLEARBACA8/fTTZu+JiYkRZs2aJQiCIHz22WeCp6enUFJSYlq+efNmQSqVChqNRhAEQQgMDBT+9a9/NVgDAOGVV14xvS4pKREACFu3bm2x7SSitsE+O0Rkk+69916sWLHCbJ6Xl5fpeWxsrNmy2NhYHD9+HABw7tw5REdHw9XV1bR80KBBMBgMSE5OhkQiQVZWFoYPH95oDVFRUabnrq6uUCqVyM3Nbe4mEZGVMOwQkU1ydXW95bRSS3F2dm5SO7lcbvZaIpHAYDC0RklE1IrYZ4eI7NL+/ftved29e3cAQPfu3XHixAmUlpaalu/duxdSqRQRERFwd3dHp06dsHPnzjatmYisg0d2iMgmVVZWQqPRmM1zcHCAj48PAGDt2rXo168fBg8ejG+//RYHDx7EF198AQCYMmUKFi5ciGnTpmHRokXIy8vDs88+i6lTp8Lf3x8AsGjRIjz99NPw8/PD6NGjUVxcjL179+LZZ59t2w0lolbHsENENmnbtm0ICAgwmxcREYHz588DMI6U+v777/HMM88gICAA3333HSIjIwEALi4u+O233/D888+jf//+cHFxwaRJk7Bs2TLTuqZNm4aKigq8//77ePHFF+Hj44PJkye33QYSUZuRCIIgWLsIIiJLSCQSbNiwAePHj7d2KURkB9hnh4iIiESNYYeIiIhEjX12iMju8Ow7EVmCR3aIiIhI1Bh2iIiISNQYdoiIiEjUGHaIiIhI1Bh2iIiISNQYdoiIiEjUGHaIiIhI1Bh2iIiISNT+P60v6qrbgro2AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_loss(train_all_loss3,test_all_loss3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f5e1eb1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_acc(train_ACC13,test_ACC13)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26aab875",
   "metadata": {},
   "source": [
    "## 利用torch.nn实现前馈神经网络实现回归任务"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "bcce1ae1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.optim import SGD\n",
    "from torch.nn import MSELoss\n",
    "# 利用torch.nn实现前馈神经网络-回归任务 代码\n",
    "# 定义自己的前馈神经网络\n",
    "class MyNet21(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(MyNet21, self).__init__()\n",
    "        # 设置隐藏层和输出层的节点数\n",
    "        num_inputs, num_hiddens, num_outputs = 500, 256, 1\n",
    "        # 定义模型结构\n",
    "        self.input_layer = nn.Flatten()\n",
    "        self.hidden_layer = nn.Linear(num_inputs, num_hiddens)\n",
    "        self.output_layer = nn.Linear(num_hiddens, num_outputs)\n",
    "        self.relu = nn.ReLU()\n",
    "\n",
    "    # 定义前向传播\n",
    "    def forward(self, x):\n",
    "        x = self.input_layer(x)\n",
    "        x = self.relu(self.hidden_layer(x))\n",
    "        x = self.output_layer(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "630c667d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 1 | train loss:150.98 | test loss:0.41\n",
      "epoch: 2 | train loss:0.69 | test loss:0.27\n",
      "epoch: 3 | train loss:0.49 | test loss:0.22\n",
      "epoch: 4 | train loss:0.40 | test loss:0.19\n",
      "epoch: 5 | train loss:0.34 | test loss:0.18\n",
      "epoch: 6 | train loss:0.30 | test loss:0.17\n",
      "epoch: 7 | train loss:0.27 | test loss:0.16\n",
      "epoch: 8 | train loss:0.25 | test loss:0.16\n",
      "epoch: 9 | train loss:0.24 | test loss:0.15\n",
      "epoch: 10 | train loss:0.22 | test loss:0.15\n",
      "epoch: 11 | train loss:0.21 | test loss:0.15\n",
      "epoch: 12 | train loss:0.20 | test loss:0.15\n",
      "epoch: 13 | train loss:0.19 | test loss:0.15\n",
      "epoch: 14 | train loss:0.18 | test loss:0.14\n",
      "epoch: 15 | train loss:0.17 | test loss:0.14\n",
      "epoch: 16 | train loss:0.17 | test loss:0.14\n",
      "epoch: 17 | train loss:0.16 | test loss:0.14\n",
      "epoch: 18 | train loss:0.15 | test loss:0.14\n",
      "epoch: 19 | train loss:0.15 | test loss:0.14\n",
      "epoch: 20 | train loss:0.14 | test loss:0.14\n",
      "torch.nn实现前馈网络-回归实验 20轮 总用时: 4.40s\n"
     ]
    }
   ],
   "source": [
    "# 训练\n",
    "model21 = MyNet21()  # logistics模型\n",
    "model21 = model21.to(device)\n",
    "criterion = MSELoss()  # 损失函数\n",
    "criterion = criterion.to(device)\n",
    "optimizer = SGD(model21.parameters(), lr=0.1)  # 优化函数\n",
    "epochs = 20  # 训练轮数\n",
    "train_all_loss21 = []  # 记录训练集上得loss变化\n",
    "test_all_loss21 = []  # 记录测试集上的loss变化\n",
    "begintime21 = time.time()\n",
    "for epoch in range(epochs):\n",
    "    train_l = 0\n",
    "    for data, labels in traindataloader1:\n",
    "        data, labels = data.to(device=device), labels.to(device)\n",
    "        pred = model21(data)\n",
    "        train_each_loss = criterion(pred.view(-1, 1), labels.view(-1, 1))  # 计算每次的损失值\n",
    "        optimizer.zero_grad()  # 梯度清零\n",
    "        train_each_loss.backward()  # 反向传播\n",
    "        optimizer.step()  # 梯度更新\n",
    "        train_l += train_each_loss.item()\n",
    "    train_all_loss21.append(train_l)  # 添加损失值到列表中\n",
    "    with torch.no_grad():\n",
    "        test_loss = 0\n",
    "        for data, labels in testdataloader1:\n",
    "            data, labels = data.to(device), labels.to(device)\n",
    "            pred = model21(data)\n",
    "            test_each_loss = criterion(pred,labels)\n",
    "            test_loss += test_each_loss.item()\n",
    "        test_all_loss21.append(test_loss)\n",
    "        print('epoch: %d | train loss:%.2f | test loss:%.2f' % (epoch + 1, train_all_loss21[-1], test_all_loss21[-1]))\n",
    "endtime21 = time.time()\n",
    "print(\"torch.nn实现前馈网络-回归实验 %d轮 总用时: %.2fs\" % (epochs, endtime21 - begintime21))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a93787b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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O7fx+fgCAVFlZqcLCQmVmZiomJsbq4sBPGruvTf3+trRm5+DBg/rqq6/M155qtOTkZHXo0OGE9BcVFaXU1FR16dJFktStWzfl5ubquuuu04IFC1RTU6Px48dr5MiRTQ46weCMZfg5AABWsXTo+ZYtW9SrVy/16tVLUl01Wq9evTR16tQmn2Px4sXq2rWrBgwYoEsuuUT9+/fXk08+Gagi++TYkhEMPwcAINgsrdm58MIL1ZxWtPraG5OTk0NmAsGGeIafl1GzAwBA0LE2VhCYNTtMLAgAQNARdoLASTMWAARNiIy7gZ/4434SdoLAnEWZmh0ACBjPzMLV1XQZaE0OHTokSS2adTlk59lpTZI862Md5g8QAAIlMjJScXFx+u677xQVFWXON4PwZBiGDh06pJKSErlcLjPM+oKwEwROc+VzanYAIFBsNpvS0tJUWFioXbt2WV0c+ElLV26XCDtBQTMWAARHdHS0OnfuTFNWKxEVFdWiGh0Pwk4QuI5rxmLlcwAILLvdzgzK8EKDZhB4wk5NraFD1b6vQAsAAJqPsBMEsVERio6o+1Ez/BwAgOAi7ASBzWY7NtcOsygDABBUhJ0gMYef00kZAICgIuwEiWdEFsPPAQAILsJOkBxbMoJmLAAAgomwEySuWBYDBQDACoSdIDk21w5hBwCAYCLsBIkrzjOLMs1YAAAEE2EnSJw0YwEAYAnCTpAkeWp2aMYCACCoCDtB4mJSQQAALEHYCRKasQAAsAZhJ0jMmp3DNTIMw+LSAABw6iDsBIlnNFb1Ebcqa9wWlwYAgFMHYSdI4qMjFGm3SWIWZQAAgomwEyQ2m+24uXbotwMAQLAQdoLI02/nB0ZkAQAQNISdIPKsj1VGzQ4AAEFD2Ami40dkAQCA4CDsBJEzlj47AAAEG2EniI7V7NBnBwCAYCHsBBF9dgAACD7CThC54mnGAgAg2Ag7QeSp2WHoOQAAwUPYCSJPn50yRmMBABA0loaddevWafDgwUpPT5fNZtOyZcvMfTU1NZo0aZJ69Oih+Ph4paen63e/+5327t3rdY79+/crLy9PiYmJcrlcGjNmjA4ePBjkK2kaF6OxAAAIOkvDTkVFhXr27Kl58+adsO/QoUPatm2bpkyZom3btunVV19VQUGBLrvsMq/j8vLy9MUXX2jlypV64403tG7dOo0dOzZYl9AsjMYCACD4bIZhGFYXQqpbO2rp0qUaOnRog8ds3rxZffv21a5du9ShQwft2LFDZ511ljZv3qzzzz9fkrRixQpdcskl+vbbb5Went6kzy4vL5fT6VRZWZkSExP9cTn1f05ljc6Z/q4k6Z/35ComKiJgnwUAQGvX1O/vsOqzU1ZWVregpsslScrPz5fL5TKDjiTl5OTIbrdr48aNDZ6nqqpK5eXlXo9gSHBEKuLoyuf02wEAIDjCJuxUVlZq0qRJuuqqq8z0VlRUpPbt23sdFxkZqeTkZBUVFTV4rhkzZsjpdJqPjIyMgJbdw2azmSOy6LcDAEBwhEXYqamp0RVXXCHDMDR//vwWn2/y5MkqKyszH3v27PFDKZvGycrnAAAEVaTVBTgZT9DZtWuX1qxZ49Uml5qaqpKSEq/jjxw5ov379ys1NbXBczocDjkcjoCVuTHU7AAAEFwhXbPjCTo7d+7UqlWr1LZtW6/92dnZKi0t1datW81ta9askdvtVlZWVrCL2ySuuLrh52WMyAIAICgsrdk5ePCgvvrqK/N1YWGhtm/fruTkZKWlpenyyy/Xtm3b9MYbb6i2ttbsh5OcnKzo6Gh169ZNubm5uu6667RgwQLV1NRo/PjxGjlyZJNHYgUbNTsAAASXpWFny5Yt+vWvf22+njhxoiRp1KhRmj59upYvXy5JOvfcc73e99577+nCCy+UJC1evFjjx4/XgAEDZLfbNWLECM2ZMyco5feF05xrh7ADAEAwWBp2LrzwQjU2zU9TpgBKTk7W888/789iBRSzKAMAEFwh3WenNUqK96yPRZ8dAACCgbATZE7PyucV1OwAABAMhJ0g84zGos8OAADBQdgJMs9orDImFQQAICgIO0HmYjQWAABBRdgJMs9orEPVtao6UmtxaQAAaP0IO0GWEBOpowufs/I5AABBQNgJMrvdZo7IYq4dAAACj7BjAXNEFmEHAICAI+xY4FjNDiOyAAAINMKOBRiRBQBA8BB2LHBsrh3CDgAAgUbYscCxWZRpxgIAINAIOxZgNBYAAMFD2LFAUhxhBwCAYCHsWIBmLAAAgoewYwEnNTsAAAQNYccCLvrsAAAQNIQdC3iasVgbCwCAwCPsWMBTs3Ow6ohqat0WlwYAgNaNsGOBxNgo2Vj5HACAoCDsWCDCblNiDOtjAQAQDIQdi7gYkQUAQFAQdizCiCwAAIKDsGMRpzmxIGEHAIBAIuxY5FjNDn12AAAIJMKORTx9dhiNBQBAYBF2LGKuj0WfHQAAAoqwYxFPM9YPNGMBABBQhB2L0IwFAEBwEHYswjw7AAAEB2HHIs5Yz9BzmrEAAAgkwo5FqNkBACA4CDsW8XRQPlB5REdY+RwAgICxNOysW7dOgwcPVnp6umw2m5YtW+a13zAMTZ06VWlpaYqNjVVOTo527tzpdcz+/fuVl5enxMREuVwujRkzRgcPHgziVfjGeTTsSFJ55RELSwIAQOtmadipqKhQz549NW/evHr3z5w5U3PmzNGCBQu0ceNGxcfHa+DAgaqsrDSPycvL0xdffKGVK1fqjTfe0Lp16zR27NhgXYLPIiPsSoiJlMTwcwAAAinSyg8fNGiQBg0aVO8+wzA0e/Zs3XnnnRoyZIgkadGiRUpJSdGyZcs0cuRI7dixQytWrNDmzZt1/vnnS5Lmzp2rSy65RA899JDS09PrPXdVVZWqqqrM1+Xl5X6+sqZxxUXpQOUR+u0AABBAIdtnp7CwUEVFRcrJyTG3OZ1OZWVlKT8/X5KUn58vl8tlBh1JysnJkd1u18aNGxs894wZM+R0Os1HRkZG4C6kEa6jI7LKGJEFAEDAhGzYKSoqkiSlpKR4bU9JSTH3FRUVqX379l77IyMjlZycbB5Tn8mTJ6usrMx87Nmzx8+lbxpGZAEAEHiWNmNZxeFwyOFwWF0Ms5MyYQcAgMAJ2Zqd1NRUSVJxcbHX9uLiYnNfamqqSkpKvPYfOXJE+/fvN48JZWbNDktGAAAQMCEbdjIzM5WamqrVq1eb28rLy7Vx40ZlZ2dLkrKzs1VaWqqtW7eax6xZs0Zut1tZWVlBL3NzJZkrn9NnBwCAQLG0GevgwYP66quvzNeFhYXavn27kpOT1aFDB02YMEH33nuvOnfurMzMTE2ZMkXp6ekaOnSoJKlbt27Kzc3VddddpwULFqimpkbjx4/XyJEjGxyJFUpoxgIAIPAsDTtbtmzRr3/9a/P1xIkTJUmjRo3Ss88+qz/96U+qqKjQ2LFjVVpaqv79+2vFihWKiYkx37N48WKNHz9eAwYMkN1u14gRIzRnzpygX4svXJ6aHZqxAAAIGJthGIbVhbBaeXm5nE6nysrKlJiYGLTPXfVlsX6/aIt6nubUa+P7B+1zAQBoDZr6/R2yfXZOBXRQBgAg8Ag7FmKeHQAAAo+wYyHn0RmUyytrVOs+5VsTAQAICMKOhTw1O4YhldOUBQBAQBB2LBQVYVcbR92AOPrtAAAQGIQdix2ba4eJBQEACATCjsUYkQUAQGARdizmCTtljMgCACAgCDsWc8WyPhYAAIFE2LEYzVgAAAQWYcdiTCwIAEBgEXYsRjMWAACBRdixmJNmLAAAAoqwYzFXLM1YAAAEEmHHYq64umasMmp2AAAICMKOxY51UKbPDgAAgUDYsZg5qeDhGrlZ+RwAAL8j7FjMszaW25AOVB6xuDQAALQ+hB2LOSIjFBcdIUkqPUxTFgAA/kbYCQGMyAIAIHAIOyHAeXREFnPtAADgf4SdEHCsZodmLAAA/I2wEwKOH5EFAAD8i7ATAjwTC/5QQdgBAMDfCDshwJxYkNFYAAD4HWEnBHj67JQxGgsAAL8j7IQAFyufAwAQMISdEOCMPTr0nNFYAAD4HWEnBFCzAwBA4BB2QoA59Jw+OwAA+B1hJwQkHTeDsmGw8jkAAP5E2AkBnpXPa92GDlSx8jkAAP5E2AkBMVERiomquxU0ZQEA4F8hHXZqa2s1ZcoUZWZmKjY2VmeccYbuuecer6YewzA0depUpaWlKTY2Vjk5Odq5c6eFpfaNyxyRRdgBAMCfQjrsPPjgg5o/f74ee+wx7dixQw8++KBmzpypuXPnmsfMnDlTc+bM0YIFC7Rx40bFx8dr4MCBqqystLDkzccsygAABEak1QVozIcffqghQ4bo0ksvlSR16tRJL7zwgjZt2iSprlZn9uzZuvPOOzVkyBBJ0qJFi5SSkqJly5Zp5MiRlpW9uZzmyufU7AAA4E8+1ezs2bNH3377rfl606ZNmjBhgp588km/FUySLrjgAq1evVr/+te/JEmffPKJPvjgAw0aNEiSVFhYqKKiIuXk5JjvcTqdysrKUn5+foPnraqqUnl5udfDasy1AwBAYPgUdv73f/9X7733niSpqKhIv/nNb7Rp0yb9+c9/1t133+23wt1xxx0aOXKkunbtqqioKPXq1UsTJkxQXl6e+dmSlJKS4vW+lJQUc199ZsyYIafTaT4yMjL8VmZfmcPPK2jGAgDAn3wKO59//rn69u0rSXr55Zd19tln68MPP9TixYv17LPP+q1wL7/8shYvXqznn39e27Zt03PPPaeHHnpIzz33XIvOO3nyZJWVlZmPPXv2+KnEvnNSswMAQED41GenpqZGDodDkrRq1SpddtllkqSuXbtq3759fivc7bffbtbuSFKPHj20a9cuzZgxQ6NGjVJqaqokqbi4WGlpaeb7iouLde655zZ4XofDYZY/VDAaCwCAwPCpZqd79+5asGCB1q9fr5UrVyo3N1eStHfvXrVt29ZvhTt06JDsdu8iRkREyO12S5IyMzOVmpqq1atXm/vLy8u1ceNGZWdn+60cwWAuGcFoLAAA/Mqnmp0HH3xQw4YN06xZszRq1Cj17NlTkrR8+XKzecsfBg8erPvuu08dOnRQ9+7d9fHHH+uRRx7RtddeK0my2WyaMGGC7r33XnXu3FmZmZmaMmWK0tPTNXToUL+VIxhcjMYCACAgfAo7F154ob7//nuVl5crKSnJ3D527FjFxcX5rXBz587VlClTdOONN6qkpETp6en6wx/+oKlTp5rH/OlPf1JFRYXGjh2r0tJS9e/fXytWrFBMTIzfyhEM9NkBACAwbIYPK08ePnxYhmGYwWbXrl1aunSpunXrpoEDB/q9kIFWXl4up9OpsrIyJSYmWlKGHfvKNegv69WujUNb7sw5+RsAADjFNfX726c+O0OGDNGiRYskSaWlpcrKytLDDz+soUOHav78+b6V+BRnzrNzqJqVzwEA8COfws62bdv0i1/8QpL0yiuvKCUlRbt27dKiRYs0Z84cvxbwVOEZjXXEbaiiutbi0gAA0Hr4FHYOHTqkhIQESdK7776r4cOHy2636+c//7l27drl1wKeKmKi7IqOrLsdpYcYkQUAgL/4FHZ+9rOfadmyZdqzZ4/eeecdXXzxxZKkkpISy/q8hDubzcaILAAAAsCnsDN16lTddttt6tSpk/r27WvOafPuu++qV69efi3gqeTYXDuEHQAA/MWnoeeXX365+vfvr3379plz7EjSgAEDNGzYML8V7lTDLMoAAPifT2FHklJTU5Wammqufn7aaaf5dULBU9Gxlc/pswMAgL/41Izldrt19913y+l0qmPHjurYsaNcLpfuuececykHNN+x4efU7AAA4C8+1ez8+c9/1tNPP60HHnhA/fr1kyR98MEHmj59uiorK3Xffff5tZCnClecpxmLmh0AAPzFp7Dz3HPP6a9//au52rkknXPOOfrpT3+qG2+8kbDjIyejsQAA8DufmrH279+vrl27nrC9a9eu2r9/f4sLdapysT4WAAB+51PY6dmzpx577LETtj/22GM655xzWlyoU5VnNFYZNTsAAPiNT81YM2fO1KWXXqpVq1aZc+zk5+drz549euutt/xawFMJo7EAAPA/n2p2fvWrX+lf//qXhg0bptLSUpWWlmr48OH64osv9Le//c3fZTxleMLOD9TsAADgNzbDj0tsf/LJJzrvvPNUWxteC1k2dYn4QPtP6WH1e2CNoiPsKrg3VzabzbKyAAAQ6pr6/e1TzQ4Cw7M2VnWtW4drwiswAgAQqgg7ISQuOkJREXW1OQw/BwDAPwg7IcRms8nJ+lgAAPhVs0ZjDR8+vNH9paWlLSkLVNdJ+fuDVYzIAgDAT5oVdpxO50n3/+53v2tRgU51nn47zLUDAIB/NCvsLFy4MFDlwFGe9bEYfg4AgH/QZyfEMLEgAAD+RdgJMTRjAQDgX4SdEGPW7BB2AADwC8JOiHEe7bNDMxYAAP5B2AkxnmYsanYAAPAPwk6I8TRjlR0m7AAA4A+EnRCTZA49pxkLAAB/IOyEGCfNWAAA+BVhJ8R4mrGqjrhVycrnAAC0GGEnxLRxRCrCzsrnAAD4C2EnxNhstmMjshh+DgBAixF2QpCTiQUBAPAbwk4IYq4dAAD8J+TDzn/+8x/93//9n9q2bavY2Fj16NFDW7ZsMfcbhqGpU6cqLS1NsbGxysnJ0c6dOy0scct5hp+XMvwcAIAWC+mw88MPP6hfv36KiorS22+/rS+//FIPP/ywkpKSzGNmzpypOXPmaMGCBdq4caPi4+M1cOBAVVZWWljyljGbsZhYEACAFou0ugCNefDBB5WRkaGFCxea2zIzM83nhmFo9uzZuvPOOzVkyBBJ0qJFi5SSkqJly5Zp5MiRQS+zP7hiPTU7hB0AAFoqpGt2li9frvPPP1+//e1v1b59e/Xq1UtPPfWUub+wsFBFRUXKyckxtzmdTmVlZSk/P7/B81ZVVam8vNzrEUqOLRlBMxYAAC0V0mHn3//+t+bPn6/OnTvrnXfe0Q033KCbb75Zzz33nCSpqKhIkpSSkuL1vpSUFHNffWbMmCGn02k+MjIyAncRPnAxGgsAAL8J6bDjdrt13nnn6f7771evXr00duxYXXfddVqwYEGLzjt58mSVlZWZjz179vipxP7BkhEAAPhPSIedtLQ0nXXWWV7bunXrpt27d0uSUlNTJUnFxcVexxQXF5v76uNwOJSYmOj1CCUsBgoAgP+EdNjp16+fCgoKvLb961//UseOHSXVdVZOTU3V6tWrzf3l5eXauHGjsrOzg1pWfzrWZ4eaHQAAWiqkR2PdcsstuuCCC3T//ffriiuu0KZNm/Tkk0/qySeflFS3tMKECRN07733qnPnzsrMzNSUKVOUnp6uoUOHWlv4FmA0FgAA/hPSYadPnz5aunSpJk+erLvvvluZmZmaPXu28vLyzGP+9Kc/qaKiQmPHjlVpaan69++vFStWKCYmxsKSt4xnnp3DNbWqrKlVTFSExSUCACB82QzDMKwuhNXKy8vldDpVVlYWEv133G5DP/vzW3Ib0qb/N0DtE8M3uAEAEChN/f4O6T47pyq73XZsRBb9dgAAaBHCTohyxdFvBwAAfyDshCjPiCyGnwMA0DKEnRDlOtqMVUbNDgAALULYCVFmMxbrYwEA0CKEnRDFkhEAAPgHYSdEmYuBMhoLAIAWIeyEKPrsAADgH4SdEEWfHQAA/IOwE6LMoecV1OwAANAShJ0Q5anZYeVzAABahrATolzmaCyasQAAaAnCTojyNGNVVNeq+ojb4tIAABC+CDshKiEmSjZb3XOasgAA8B1hJ0RF2G1KjDk6/JwRWQAA+IywE8LMiQWZawcAAJ8RdkKYZ0TWD4QdAAB8RtgJYYzIAgCg5Qg7IczTjEUHZQAAfEfYCWEuVj4HAKDFCDshzMn6WAAAtBhhJ4RRswMAQMsRdkIYfXYAAGg5wk4ISzKHntOMBQCArwg7IczJpIIAALQYYSeEefrslBF2AADwGWEnhHlmUD5QdUQ1tax8DgCALwg7ISwxJtJ8Xk4nZQAAfELYCWGREXYlHA08pYQdAAB8QtgJcZ4RWayPBQCAbwg7Ic7FiCwAAFqEsBPinMyiDABAixB2QpzLXB+LsAMAgC8IOyHu2Fw79NkBAMAXYRV2HnjgAdlsNk2YMMHcVllZqXHjxqlt27Zq06aNRowYoeLiYusK6Wdmnx1qdgAA8EnYhJ3NmzfriSee0DnnnOO1/ZZbbtHrr7+uJUuWaO3atdq7d6+GDx9uUSn9jz47AAC0TFiEnYMHDyovL09PPfWUkpKSzO1lZWV6+umn9cgjj+iiiy5S7969tXDhQn344Yf66KOPGjxfVVWVysvLvR6hisVAAQBombAIO+PGjdOll16qnJwcr+1bt25VTU2N1/auXbuqQ4cOys/Pb/B8M2bMkNPpNB8ZGRkBK3tLeZqxymjGAgDAJyEfdl588UVt27ZNM2bMOGFfUVGRoqOj5XK5vLanpKSoqKiowXNOnjxZZWVl5mPPnj3+LrbfMM8OAAAtE3nyQ6yzZ88e/fGPf9TKlSsVExPjt/M6HA45HA6/nS+QnLHMoAwAQEuEdM3O1q1bVVJSovPOO0+RkZGKjIzU2rVrNWfOHEVGRiolJUXV1dUqLS31el9xcbFSU1OtKbSfeWp2yiuPqNZtWFwaAADCT0jX7AwYMECfffaZ17ZrrrlGXbt21aRJk5SRkaGoqCitXr1aI0aMkCQVFBRo9+7dys7OtqLIfucZjSXVrXyeFB9tYWkAAAg/IR12EhISdPbZZ3tti4+PV9u2bc3tY8aM0cSJE5WcnKzExETddNNNys7O1s9//nMriux3URF2tXFE6mDVEZUSdgAAaLaQDjtN8eijj8put2vEiBGqqqrSwIED9fjjj1tdLL9yxUXpYNUR/XCoWpmKt7o4AACElbALO++//77X65iYGM2bN0/z5s2zpkBB4IqL0rc/HFYZI7IAAGi2kO6gjDouz4isw4zIAgCguQg7YcDJXDsAAPiMsBMGXKyPBQCAzwg7YYAlIwAA8B1hJwy4mEUZAACfEXbCgKdm5weasQAAaDbCThhwxXlGYxF2AABoLsJOGDD77NCMBQBAsxF2woA5GouaHQAAmo2wEwacx43GcrPyOQAAzULYCQOelc8NQzpQecTi0gAAEF4IO2HAERmhuOgISdIP9NsBAKBZCDthIokRWQAA+ISwEyac5pIR1OwAANAchJ0wwZIRAAD4hrATJlysfA4AgE8IO2HCaa6PRdgBAKA5CDthwqzZOUyfHQAAmoOwEyaSaMYCAMAnhJ0w4TKbsajZAQCgOQg7YcIZx/pYAAD4grATJjyLgZbRjAUAQLMQdsKEixmUAQDwCWEnTBybZ6ealc8BAGgGwk6Y8CwX4Takg9WsfA4AQFMRdsJETFSEYqPqVj4vraApCwCApiLshBEmFgQAoPkIO2Hk2Mrn1OwAANBUhJ0w4mKuHQAAmo2wE0Y8syiXMYsyAABNRtgJIy7WxwIAoNkIO2GEJSMAAGg+wk4YSTo6i/IPNGMBANBkIR12ZsyYoT59+ighIUHt27fX0KFDVVBQ4HVMZWWlxo0bp7Zt26pNmzYaMWKEiouLLSpxYLE+FgAAzRfSYWft2rUaN26cPvroI61cuVI1NTW6+OKLVVFRYR5zyy236PXXX9eSJUu0du1a7d27V8OHD7ew1IHDaCwAAJov0uoCNGbFihVer5999lm1b99eW7du1S9/+UuVlZXp6aef1vPPP6+LLrpIkrRw4UJ169ZNH330kX7+859bUeyAcR4djVVKMxYAAE0W0jU7P1ZWViZJSk5OliRt3bpVNTU1ysnJMY/p2rWrOnTooPz8/AbPU1VVpfLycq9HOPDU7JRRswMAQJOFTdhxu92aMGGC+vXrp7PPPluSVFRUpOjoaLlcLq9jU1JSVFRU1OC5ZsyYIafTaT4yMjICWXS/OX7ouWGw8jkAAE0RNmFn3Lhx+vzzz/Xiiy+2+FyTJ09WWVmZ+dizZ48fShh4nkkFj7gNVVTXWlwaAADCQ0j32fEYP3683njjDa1bt06nnXaauT01NVXV1dUqLS31qt0pLi5Wampqg+dzOBxyOByBLHJAxEZHyBFpV9URt36oqFYbR1jcPgAALBXSNTuGYWj8+PFaunSp1qxZo8zMTK/9vXv3VlRUlFavXm1uKygo0O7du5WdnR3s4gYF/XYAAGiekK4aGDdunJ5//nm99tprSkhIMPvhOJ1OxcbGyul0asyYMZo4caKSk5OVmJiom266SdnZ2a1uJJaHKzZaxeVVLBkBAEAThXTYmT9/viTpwgsv9Nq+cOFCjR49WpL06KOPym63a8SIEaqqqtLAgQP1+OOPB7mkwXNsyQiGnwMA0BQhHXaaMuIoJiZG8+bN07x584JQIut5ZlGmZgcAgKYJ6T47OBF9dgAAaB7CTphxeRYDraAZCwCApiDshBnWxwIAoHkIO2HGZa6PRdgBAKApCDth5lifHZqxAABoCsJOmGE0FgAAzUPYCTNO+uwAANAshJ0w4xmNVcbK5wAANAlhJ8wkHa3Zqa516xArnwMAcFKEnTATGxWh6Ii620ZTFgAAJ0fYCTM2m+1Yv51DjMgCAOBkCDthyDMiq4wRWQAAnBRhJwwxizIAAE1H2AlDTmZRBgCgyQg7YehYzQ59dgAAOBnCThhKimMWZQAAmoqwE4Y8EwsyGgsAgJMj7IQhJ+tjAQDQZISdMMRoLAAAmo6wE4ZcscfWxwIAAI0j7IQhRmMBANB0hJ0wRJ8dAACajrAThpLi65qxqo64dZiVzwEAaBRhJwzFR0co0m6TRFMWAAAnQ9gJQzab7Vi/HZqyAABoFGEnTNFvBwCApiHshCnPLMplNGMBANAowk6YclGzAwBAkxB2wpSTWZQBAGgSwk6YSjrajPUDi4ECANAowk6Y8jRjsWQEAACNI+yEKYaeAwDQNISdMOU82ozFpIIAADSOsBOmGI0FAEDTRFpdAH+ZN2+eZs2apaKiIvXs2VNz585V3759rS5WwHiasUoOVGnF50WKirApMsKuSLut7hFhV1SETRF2m6KObo+KsCvSs81e99yzL8Juk81ms/iqAADwv1YRdl566SVNnDhRCxYsUFZWlmbPnq2BAweqoKBA7du3t65gr42XyvdK9gjJFlH3X/N5ZOPbvfZFHn1uN593qKzV1RH/llFp0/oX3pIhmwzp6H+PPZdnm9HQfpvcR7fZbTbZ7TbZ7XbZ7HbZbXbZ7JJdNslWF4Y8D9lssh19j47bZrdJstmP7jv63CbZbPajx9Zts9skm2zH9ktHzyPz3OY26djnHn2u497jKZ+ko5959P11BxwLceZnHi2G51zmNs/5Zf73+OPrruvYeX6cDY8d773DPPa47Z7PN6/n+JN4le34XT8q3/EnO+7Jsf31H3/stPYfvb/uqXHctR1fgh9//nFnPOFnUbfZXs856jvvj7f8+EdoO2H7jz/uhJ/VCWVp9KX5s6rvgMY+68R99Z/khN+Vk5Sv3jLVc5762GwnVtjX9756r6IJBavvH0X13v6T3ZMGytXQRTb3n2IN/azq+7k2enwT783RHc3Z3Mj9bF4ZG35X/f8f0pwztOzzvbX/aSdFRTtOclRg2AzDMCz5ZD/KyspSnz599Nhjj0mS3G63MjIydNNNN+mOO+444fiqqipVVVWZr8vLy5WRkaGysjIlJib6r2Bze0v//cp/5wMAIEztyVunjM49/XrO8vJyOZ3Ok35/h33NTnV1tbZu3arJkyeb2+x2u3JycpSfn1/ve2bMmKG77ror8IW7+F6pskxyH5HctZJRW/df8/mRH70+uu2E447f7j7u+RHJcEuevGrU1dec7L+GcfzDXfdft/vYc6PuWMMwZHje53mv53OOfqbhdW6Zz40TynPcvuNfm/85yTYzkhvm/9p+dB7PNs9W23HbJclmnvpYvj/+XyLHjvPO/8Zx56rv9bEd9f274cRtx5+/wXM1WwPnMOp/2fC/wOo/T8NlNOp96p9rql9zz23Jv+Ys+ydk8z64uT/LQN7XYJz/VNLgT7KBHUH52ddT6xgsYR92vv/+e9XW1iolJcVre0pKiv75z3/W+57Jkydr4sSJ5mtPzY7fdRnk/3P6wfFNKAAABEMAvmWbLOzDji8cDoccDmvaDQEAQHCF/dDzdu3aKSIiQsXFxV7bi4uLlZqaalGpAABAqAj7sBMdHa3evXtr9erV5ja3263Vq1crOzvbwpIBAIBQ0CqasSZOnKhRo0bp/PPPV9++fTV79mxVVFTommuusbpoAADAYq0i7Fx55ZX67rvvNHXqVBUVFencc8/VihUrTui0DAAATj2tYp6dlmrqOH0AABA6mvr9HfZ9dgAAABpD2AEAAK0aYQcAALRqhB0AANCqEXYAAECrRtgBAACtGmEHAAC0aoQdAADQqrWKGZRbyjOvYnl5ucUlAQAATeX53j7Z/MiEHUkHDhyQJGVkZFhcEgAA0FwHDhyQ0+lscD/LRahulfS9e/cqISFBNpvNb+ctLy9XRkaG9uzZ02qXoWjt18j1hb/Wfo1cX/hr7dcYyOszDEMHDhxQenq67PaGe+ZQsyPJbrfrtNNOC9j5ExMTW+Uv8PFa+zVyfeGvtV8j1xf+Wvs1Bur6GqvR8aCDMgAAaNUIOwAAoFUj7ASQw+HQtGnT5HA4rC5KwLT2a+T6wl9rv0auL/y19msMheujgzIAAGjVqNkBAACtGmEHAAC0aoQdAADQqhF2AABAq0bYaaF58+apU6dOiomJUVZWljZt2tTo8UuWLFHXrl0VExOjHj166K233gpSSZtvxowZ6tOnjxISEtS+fXsNHTpUBQUFjb7n2Weflc1m83rExMQEqcTNM3369BPK2rVr10bfE073r1OnTidcn81m07hx4+o9Phzu3bp16zR48GClp6fLZrNp2bJlXvsNw9DUqVOVlpam2NhY5eTkaOfOnSc9b3P/jgOlseurqanRpEmT1KNHD8XHxys9PV2/+93vtHfv3kbP6cvveSCd7B6OHj36hPLm5uae9LzhcA8l1fs3abPZNGvWrAbPGUr3sCnfC5WVlRo3bpzatm2rNm3aaMSIESouLm70vL7+7TYVYacFXnrpJU2cOFHTpk3Ttm3b1LNnTw0cOFAlJSX1Hv/hhx/qqquu0pgxY/Txxx9r6NChGjp0qD7//PMgl7xp1q5dq3Hjxumjjz7SypUrVVNTo4svvlgVFRWNvi8xMVH79u0zH7t27QpSiZuve/fuXmX94IMPGjw23O7f5s2bva5t5cqVkqTf/va3Db4n1O9dRUWFevbsqXnz5tW7f+bMmZozZ44WLFigjRs3Kj4+XgMHDlRlZWWD52zu33EgNXZ9hw4d0rZt2zRlyhRt27ZNr776qgoKCnTZZZed9LzN+T0PtJPdQ0nKzc31Ku8LL7zQ6DnD5R5K8rquffv26ZlnnpHNZtOIESMaPW+o3MOmfC/ccsstev3117VkyRKtXbtWe/fu1fDhwxs9ry9/u81iwGd9+/Y1xo0bZ76ura010tPTjRkzZtR7/BVXXGFceumlXtuysrKMP/zhDwEtp7+UlJQYkoy1a9c2eMzChQsNp9MZvEK1wLRp04yePXs2+fhwv39//OMfjTPOOMNwu9317g+ne2cYhiHJWLp0qfna7XYbqampxqxZs8xtpaWlhsPhMF544YUGz9Pcv+Ng+fH11WfTpk2GJGPXrl0NHtPc3/Ngqu8aR40aZQwZMqRZ5wnnezhkyBDjoosuavSYUL6HP/5eKC0tNaKioowlS5aYx+zYscOQZOTn59d7Dl//dpuDmh0fVVdXa+vWrcrJyTG32e125eTkKD8/v9735Ofnex0vSQMHDmzw+FBTVlYmSUpOTm70uIMHD6pjx47KyMjQkCFD9MUXXwSjeD7ZuXOn0tPTdfrppysvL0+7d+9u8Nhwvn/V1dX6+9//rmuvvbbRxW7D6d79WGFhoYqKirzukdPpVFZWVoP3yJe/41BSVlYmm80ml8vV6HHN+T0PBe+//77at2+vLl266IYbbtB///vfBo8N53tYXFysN998U2PGjDnpsaF6D3/8vbB161bV1NR43Y+uXbuqQ4cODd4PX/52m4uw46Pvv/9etbW1SklJ8dqekpKioqKiet9TVFTUrONDidvt1oQJE9SvXz+dffbZDR7XpUsXPfPMM3rttdf097//XW63WxdccIG+/fbbIJa2abKysvTss89qxYoVmj9/vgoLC/WLX/xCBw4cqPf4cL5/y5YtU2lpqUaPHt3gMeF07+rjuQ/NuUe+/B2HisrKSk2aNElXXXVVo4srNvf33Gq5ublatGiRVq9erQcffFBr167VoEGDVFtbW+/x4XwPn3vuOSUkJJy0iSdU72F93wtFRUWKjo4+IYCf7LvRc0xT39NcrHqOJhk3bpw+//zzk7YTZ2dnKzs723x9wQUXqFu3bnriiSd0zz33BLqYzTJo0CDz+TnnnKOsrCx17NhRL7/8cpP+pRVOnn76aQ0aNEjp6ekNHhNO9+5UV1NToyuuuEKGYWj+/PmNHhtuv+cjR440n/fo0UPnnHOOzjjjDL3//vsaMGCAhSXzv2eeeUZ5eXknHQgQqvewqd8LoYCaHR+1a9dOERERJ/QwLy4uVmpqar3vSU1NbdbxoWL8+PF644039N577+m0005r1nujoqLUq1cvffXVVwEqnf+4XC6deeaZDZY1XO/frl27tGrVKv3+979v1vvC6d5JMu9Dc+6RL3/HVvMEnV27dmnlypWN1urU52S/56Hm9NNPV7t27RosbzjeQ0lav369CgoKmv13KYXGPWzoeyE1NVXV1dUqLS31Ov5k342eY5r6nuYi7PgoOjpavXv31urVq81tbrdbq1ev9vrX8fGys7O9jpeklStXNni81QzD0Pjx47V06VKtWbNGmZmZzT5HbW2tPvvsM6WlpQWghP518OBBff311w2WNdzun8fChQvVvn17XXrppc16XzjdO0nKzMxUamqq1z0qLy/Xxo0bG7xHvvwdW8kTdHbu3KlVq1apbdu2zT7HyX7PQ823336r//73vw2WN9zuocfTTz+t3r17q2fPns1+r5X38GTfC71791ZUVJTX/SgoKNDu3bsbvB++/O36UnD46MUXXzQcDofx7LPPGl9++aUxduxYw+VyGUVFRYZhGMbVV19t3HHHHebxGzZsMCIjI42HHnrI2LFjhzFt2jQjKirK+Oyzz6y6hEbdcMMNhtPpNN5//31j37595uPQoUPmMT++xrvuust45513jK+//trYunWrMXLkSCMmJsb44osvrLiERt16663G+++/bxQWFhobNmwwcnJyjHbt2hklJSWGYYT//TOMulEpHTp0MCZNmnTCvnC8dwcOHDA+/vhj4+OPPzYkGY888ojx8ccfm6ORHnjgAcPlchmvvfaa8emnnxpDhgwxMjMzjcOHD5vnuOiii4y5c+ear0/2dxwq11ddXW1cdtllxmmnnWZs377d62+yqqqqwes72e95sDV2jQcOHDBuu+02Iz8/3ygsLDRWrVplnHfeeUbnzp2NyspK8xzheg89ysrKjLi4OGP+/Pn1niOU72FTvheuv/56o0OHDsaaNWuMLVu2GNnZ2UZ2drbXebp06WK8+uqr5uum/O22BGGnhebOnWt06NDBiI6ONvr27Wt89NFH5r5f/epXxqhRo7yOf/nll40zzzzTiI6ONrp37268+eabQS5x00mq97Fw4ULzmB9f44QJE8yfR0pKinHJJZcY27ZtC37hm+DKK6800tLSjOjoaOOnP/2pceWVVxpfffWVuT/c759hGMY777xjSDIKCgpO2BeO9+69996r93fScx1ut9uYMmWKkZKSYjgcDmPAgAEnXHvHjh2NadOmeW1r7O84mBq7vsLCwgb/Jt977z3zHD++vpP9ngdbY9d46NAh4+KLLzZ+8pOfGFFRUUbHjh2N66677oTQEq730OOJJ54wYmNjjdLS0nrPEcr3sCnfC4cPHzZuvPFGIykpyYiLizOGDRtm7Nu374TzHP+epvzttoTt6IcCAAC0SvTZAQAArRphBwAAtGqEHQAA0KoRdgAAQKtG2AEAAK0aYQcAALRqhB0AANCqEXYAAECrRtgBgHrYbDYtW7bM6mIA8APCDoCQM3r0aNlsthMeubm5VhcNQBiKtLoAAFCf3NxcLVy40Gubw+GwqDQAwhk1OwBCksPhUGpqqtcjKSlJUl0T0/z58zVo0CDFxsbq9NNP1yuvvOL1/s8++0wXXXSRYmNj1bZtW40dO1YHDx70OuaZZ55R9+7d5XA4lJaWpvHjx3vt//777zVs2DDFxcWpc+fOWr58eWAvGkBAEHYAhKUpU6ZoxIgR+uSTT5SXl6eRI0dqx44dkqSKigoNHDhQSUlJ2rx5s5YsWaJVq1Z5hZn58+dr3LhxGjt2rD777DMtX75cP/vZz7w+46677tIVV1yhTz/9VJdccony8vK0f//+oF4nAD/w2/rpAOAno0aNMiIiIoz4+Hivx3333WcYhmFIMq6//nqv92RlZRk33HCDYRiG8eSTTxpJSUnGwYMHzf1vvvmmYbfbjaKiIsMwDCM9Pd3485//3GAZJBl33nmn+frgwYOGJOPtt9/223UCCA767AAISb/+9a81f/58r23Jycnm8+zsbK992dnZ2r59uyRpx44d6tmzp+Lj4839/fr1k9vtVkFBgWw2m/bu3asBAwY0WoZzzjnHfB4fH6/ExESVlJT4ekkALELYARCS4uPjT2hW8pfY2NgmHRcVFeX12mazye12B6JIAAKIPjsAwtJHH310wutu3bpJkrp166ZPPvlEFRUV5v4NGzbIbrerS5cuSkhIUKdOnbR69eqglhmANajZARCSqqqqVFRU5LUtMjJS7dq1kyQtWbJE559/vvr376/Fixdr06ZNevrppyVJeXl5mjZtmkaNGqXp06fru+++00033aSrr75aKSkpkqTp06fr+uuvV/v27TVo0CAdOHBAGzZs0E033RTcCwUQcIQdACFpxYoVSktL89rWpUsX/fOf/5RUN1LqxRdf1I033qi0tDS98MILOuussyRJcXFxeuedd/THP/5Rffr0UVxcnEaMGKFHHnnEPNeoUaNUWVmpRx99VLfddpvatWunyy+/PHgXCCBobIZhGFYXAgCaw2azaenSpRo6dKjVRQEQBuizAwAAWjXCDgAAaNXoswMg7ND6DqA5qNkBAACtGmEHAAC0aoQdAADQqhF2AABAq0bYAQAArRphBwAAtGqEHQAA0KoRdgAAQKv2/wHV12bNY5fXYgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_loss(train_all_loss21,test_all_loss21)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55cb52ce",
   "metadata": {},
   "source": [
    "## 利用torch.nn实现前馈神经网络实现二分类任务"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c4c74ca1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.nn.functional import binary_cross_entropy\n",
    "# 利用torch.nn实现前馈神经网络-回归任务 代码\n",
    "# 定义自己的前馈神经网络\n",
    "class MyNet22(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(MyNet22, self).__init__()\n",
    "        # 设置隐藏层和输出层的节点数\n",
    "        num_inputs, num_hiddens, num_outputs = 200, 256, 1\n",
    "        # 定义模型结构\n",
    "        self.input_layer = nn.Flatten()\n",
    "        self.hidden_layer = nn.Linear(num_inputs, num_hiddens)\n",
    "        self.output_layer = nn.Linear(num_hiddens, num_outputs)\n",
    "        self.relu = nn.ReLU()\n",
    "\n",
    "    def logistic(self, x):  # 定义logistic函数\n",
    "        x = 1.0 / (1.0 + torch.exp(-x))\n",
    "        return x\n",
    "    # 定义前向传播\n",
    "    def forward(self, x):\n",
    "        x = self.input_layer(x)\n",
    "        x = self.relu(self.hidden_layer(x))\n",
    "        x = self.logistic(self.output_layer(x))\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "4a678973",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 1 | train loss:84.45 test loss:34.68 | train acc:0.504 | test acc:0.526\n",
      "epoch: 2 | train loss:78.46 test loss:32.76 | train acc:0.548 | test acc:0.567\n",
      "epoch: 3 | train loss:74.42 test loss:31.34 | train acc:0.585 | test acc:0.600\n",
      "epoch: 4 | train loss:71.41 test loss:30.22 | train acc:0.618 | test acc:0.630\n",
      "epoch: 5 | train loss:68.97 test loss:29.33 | train acc:0.645 | test acc:0.652\n",
      "epoch: 6 | train loss:67.06 test loss:28.61 | train acc:0.666 | test acc:0.664\n",
      "epoch: 7 | train loss:65.41 test loss:28.02 | train acc:0.684 | test acc:0.681\n",
      "epoch: 8 | train loss:64.08 test loss:27.52 | train acc:0.696 | test acc:0.688\n",
      "epoch: 9 | train loss:62.91 test loss:27.09 | train acc:0.705 | test acc:0.694\n",
      "epoch: 10 | train loss:61.91 test loss:26.74 | train acc:0.713 | test acc:0.701\n",
      "epoch: 11 | train loss:61.05 test loss:26.43 | train acc:0.718 | test acc:0.707\n",
      "epoch: 12 | train loss:60.27 test loss:26.16 | train acc:0.722 | test acc:0.710\n",
      "epoch: 13 | train loss:59.70 test loss:25.94 | train acc:0.727 | test acc:0.715\n",
      "epoch: 14 | train loss:59.04 test loss:25.74 | train acc:0.731 | test acc:0.718\n",
      "epoch: 15 | train loss:58.53 test loss:25.55 | train acc:0.733 | test acc:0.721\n",
      "epoch: 16 | train loss:58.00 test loss:25.40 | train acc:0.736 | test acc:0.723\n",
      "epoch: 17 | train loss:57.55 test loss:25.26 | train acc:0.739 | test acc:0.726\n",
      "epoch: 18 | train loss:57.25 test loss:25.15 | train acc:0.741 | test acc:0.727\n",
      "epoch: 19 | train loss:56.89 test loss:25.04 | train acc:0.743 | test acc:0.728\n",
      "epoch: 20 | train loss:56.54 test loss:24.96 | train acc:0.745 | test acc:0.729\n",
      "torch.nn实现前馈网络-二分类实验 20轮 总用时: 10.59s\n"
     ]
    }
   ],
   "source": [
    "# 训练\n",
    "model22 = MyNet22()  # logistics模型\n",
    "model22 = model22.to(device)\n",
    "optimizer = SGD(model22.parameters(), lr=0.001)  # 优化函数\n",
    "epochs = 20  # 训练轮数\n",
    "train_all_loss22 = []  # 记录训练集上得loss变化\n",
    "test_all_loss22 = []  # 记录测试集上的loss变化\n",
    "train_ACC22, test_ACC22 = [], []\n",
    "begintime22 = time.time()\n",
    "for epoch in range(epochs):\n",
    "    train_l, train_epoch_count, test_epoch_count = 0, 0, 0 # 每一轮的训练损失值 训练集正确个数 测试集正确个数\n",
    "    for data, labels in traindataloader2:\n",
    "        data, labels = data.to(device), labels.to(device)\n",
    "        pred = model22(data)\n",
    "        train_each_loss = binary_cross_entropy(pred.view(-1), labels.view(-1))  # 计算每次的损失值\n",
    "        optimizer.zero_grad()  # 梯度清零\n",
    "        train_each_loss.backward()  # 反向传播\n",
    "        optimizer.step()  # 梯度更新\n",
    "        train_l += train_each_loss.item()\n",
    "        pred = torch.tensor(np.where(pred.cpu()>0.5, 1, 0))  # 大于 0.5时候，预测标签为 1 否则为0\n",
    "        each_count = (pred.view(-1) == labels.cpu()).sum() # 每一个batchsize的正确个数\n",
    "        train_epoch_count += each_count # 计算每个epoch上的正确个数\n",
    "    train_ACC22.append(train_epoch_count / len(traindataset2))\n",
    "    train_all_loss22.append(train_l)  # 添加损失值到列表中\n",
    "    with torch.no_grad():\n",
    "        test_loss, each_count = 0, 0\n",
    "        for data, labels in testdataloader2:\n",
    "            data, labels = data.to(device), labels.to(device)\n",
    "            pred = model22(data)\n",
    "            test_each_loss = binary_cross_entropy(pred.view(-1),labels)\n",
    "            test_loss += test_each_loss.item()\n",
    "            # .cpu 为转换到cpu上计算\n",
    "            pred = torch.tensor(np.where(pred.cpu() > 0.5, 1, 0))\n",
    "            each_count = (pred.view(-1)==labels.cpu().view(-1)).sum()\n",
    "            test_epoch_count += each_count\n",
    "        test_all_loss22.append(test_loss)\n",
    "        test_ACC22.append(test_epoch_count / len(testdataset2))\n",
    "        print('epoch: %d | train loss:%.2f test loss:%.2f | train acc:%.3f | test acc:%.3f' % (epoch + 1, train_all_loss22[-1],test_all_loss22[-1], train_ACC22[-1], test_ACC22[-1]))\n",
    "endtime22 = time.time()\n",
    "print(\"torch.nn实现前馈网络-二分类实验 %d轮 总用时: %.2fs\" % (epochs, endtime22 - begintime22))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "10b22de9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_loss(train_all_loss22,test_all_loss22)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "db409dd3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZN24cfn5+jBkzhrfffptvvvmGzz77jNatW3P48GEOHz4MwG+//UZoaChz585lyJAhmM1Vz58xe/Zs7rzzTgIDAxk6dCjz5s3jhRdesD8/evRo1q1bx9tvv02vXr1ITEwkPT0dgKNHj3LFFVdw1VVXsXLlSgICAlizZg0lJSXndf4iTVVadoEt6BzNtgeeIydPVdg21N+LC6IC6R4ZQPdI2/dWzX0UcqQsw4DiU1CYDQXZpd+z/vT4rO+dh0C3YU4rVwGoLhTnw2uRjv+5f0sGT7/zOsSUKVN46623+Mtf/gJA27Zt2blzJ++//z5jxowhKSmJjh07ctlll2EymWjTpo39tS1btgQgKCioTI9SRfbu3cv69ev54osvALjzzjuZOHEizz//PCaTiT179vDZZ5+xfPly4uLiAGjXrp399e+99x6BgYEsWLAADw/bWjudOnU6r3MXaQoMw+DwiVOlISeL7Udtl7PScyseq9M62JfukQFcEBVIt8gAukcGEOrv7eCqpV5YrWAtBktx6feSsx6XnLW/9HFhTuXh5XS4+XPAsdbif0qbhSoAiXPk5eWxf/9+7r33XsaNG2ffX1JSQmBgIAB33303gwYNonPnzgwZMoQbbriBa6+9ttY/a86cOQwePJiQkBAArrvuOu69915WrlzJwIED2bJlC2azmSuvvLLC12/ZsoXLL7/cHn5EpHIZuYWs3J3G8p2prDuQQU5B+Q8lNxO0b9msTM9Ot0gt5tmgFBdAbgrkpFb8vSC7giBTRbAxKp9eoG6ZwCsAvANKvweetX3W91YXOaieiikA1QUPX1tvjDN+7nnIzc0FYNasWcTGxpZ57vTlrAsvvJDExES+//57VqxYwYgRI4iLi2Px4sU1/jkWi4X58+eTkpKCu7t7mf1z5sxh4MCB+Pj4VHmM6p4XaeoOHM9l+c5UVuxKZeOhk2VmRvY0u9E53N8WdEoDT9fwAHw8teyDUxTmnBVmUiA31fY9J+VPASer/msxmcHsAW4eYHYHs+eZbc9m5UPLn4NNRfs9m4Fbw7+LTwGoLphM530pyhnCwsKIjIzkwIED3HHHHZW2CwgIYOTIkYwcOZJbb72VIUOGcOLECYKDg/Hw8MBisVT5c7777jtycnLYvHlzmXFC27dvZ+zYsWRmZtKjRw+sViurV6+2XwI7W8+ePZk/fz7FxcXqBRIBLFaDLYdPsmxnKit2prL/eF6Z57tFBBDXLYyBXULpGhGAp3vD/0BqFE6dhBOJcDIRMg+XhptjZXtvivOqP85pZi/wD4Nm4WW/+0fYAsjpsOLmUTbI2B+7n7W/gsdNeByXAlAT99JLL/Hoo48SGBjIkCFDKCws5Pfff+fkyZNMnDiRadOmERERQZ8+fXBzc2PRokWEh4cTFBQEQExMDPHx8Vx66aV4eXnRvHnzcj9j9uzZXH/99fTq1avM/m7duvHEE0/w8ccfM2HCBMaMGcM999xjHwR96NAh0tLSGDFiBA8//DDvvPMOo0aNYtKkSQQGBrJ+/XouuugiOnfu7Ij/VCJOd6rIws97j7NiVyrxu9LIyCuyP+fuZuLidi0Y1C2MgV1DadX8/HqIpRJWqy3InA45f/5+6mTNjuPZDJqFgX/4me/+4eWDjndQkw4p9UkBqIm777778PX15c033+Tpp5/Gz8+PHj168PjjjwPg7+/PG2+8wd69ezGbzfTv35/vvvsOt9LuzbfeeouJEycya9YsoqKiyt0Gn5qaypIlS/jkk0/K/Ww3NzduvvlmZs+ezYQJE5gxYwZ/+9vfeOihh8jIyKB169b87W9/A6BFixasXLmSp59+miuvvBKz2Uzv3r259NJL6/W/j4izHc8pZOXuVJbvTOXnvekUlpwZx+Hv7c7VnUOJ6xbGVZ1bEuCt3tE6YSmGzKRKQs5BKKn4bjk7v1AIbgtBbUqDTcRZoaY08Hg5Zv41qZzJMCqa4aFpy87OJjAwkKysLAICAso8V1BQQGJiIm3btsXbW3dGuBq9f9LQGYbB/uO5LN+ZxvKdKWw+nFlmHp6oIB8GdQtjULcw+scE69LWuTAM2/iarMMVh5ysI2BUcWnfZIbAVraQE9wOmre1bTdvC81jFG6cqKrP7z9TD5CIiJNZrAYbD51k+c4UVuxKIzG97BiRHlGBDOoWRlzXMLpG+Gv+nepYSiAn2RZkso7Ygs7p7czS7aKcqo/h7nMm1ASXBpvTj4Na28bPiEtTABIRcYLkzFOsP5DBL/vS+XF3Gifzi+3PeZrdGNC+BXHdwojrGkpEoO6CLKMg66wwc1a4Of2Vk1yzW759gv8Ucs767h+usTeNnAKQiIgDpGQVsP5ABuv2Z7A+MYNDGWVnjw/08eCaLqEM6hbG5R1D8G/K43ksxZCxD9J22sbc/DngFGZXfwyzJwRE2S5VBUaXfm8FQdG2xwFR4KmB4k2ZApCISD1Iyy5g3YEM1h/IYP2BE+Uua7mZoEerIC5uF8xVnULpH9Mc96a2ArphQHayLeik7rB9pe2E9D1gKar6tb4tyoebwOgzj/1ausRcNOI8CkDnSGPHXZPeN6kvaTkF/HrghD30HDhePvB0jwxkQPsWDGjXgn4xzZtWL09BNqTtgrTSoJO607Zd2WR/nv4Q1g2C25/Vc9NKvTdSZxSAaun0JHz5+fmandgFFRXZ/q+yuoVbRaqTnltYGnjSWX/gBPvScss8bzLZJiMc0K4FF7drQf+2wU1jmYnTl6/O7tFJ3QlZSRW3d3OHFh1tYSe0G4R1t30FRmsMjtQrBaBaMpvNBAUFkZaWBoCvr6/uyHARVquV48eP4+vrW2ZJDpGaOJFXxK8HMuw9PHtSc8u16WoPPMHEtm1BoG8jDzw5KZCyreaXrwKiSkNONwgtDTohHcHdy7F1i6AAdE5Or3x+OgSJ63Bzc6N169YKrVKtEouV3w+dZNmOVNbuT2d3SvnbpruE+3NxaQ9PbNtgmvt5OqFSB8k/AcmbIXkTHN1s286pZA3E05evTvfonA49PuVnihdxFgWgc2AymYiIiCA0NJTi4uLqXyANhqenp30Wa5E/Kyi28MvedH7YkcKKXallbk0H6BTWzH5JK7ZdC4Iba+ApzIFjf8DRTWdCz8mD5duZ3CCk01khp/R7UGtdvpIGTwHoPJjNZo0lEXFxWaeK+XF3Gj/sSGH1nuPkF52ZATjI14OBXcK4uktLLm7XgpBmjfBSTXGB7TKWvXdnk+0yFhXcMBDcHqIuhMg+EHkhRPR0yYWgRUABSESaoNTsApbtTGXZjhTW7c+gxHrmwz4i0JvB3cO5tlsYF7UNbly3pluKbXdinQ46yZtt43asJeXbBrSCqNKgE3UhRPTSJSxpVBSARKRJ2H88l2U7UvlhRwpbDmeWea5jaDNb6OkeRo+owMYzRiw7GRJ/Kg07m2w9PSUF5dv5tbQFncg+Z3p4moU6vl4RB1IAEpFGyTAMth7JYtnOFH7YkVruNvU+rYPsPT3tWjaSxSsNwxZyEr6HhO/g2JbybbwCIbJ32UtZga00ZkeaHAUgEWk0ii1WNiSeYNmOFJbtTOVY1pneDnc3EwPat2Bw93AGdQsjLMDbiZXWoZJCOPhzaehZCtlHznrSZAs60bFneniC22mGZBEUgETExRUUW1iVcJxlO1OI35VG1qkzd275epq5qnNLBncP56rOoY1nIsL8E7B3ma2XZ188FJ3Vu+XhC+2uhs5DodNgXcoSqYQCkIi4pMMn8vnv+kMs/O1wmdAT7OdJXNdQBncP59IOIXh7NJI7NdP32QJPwvdweH3Z1c6bhUPnIdD5Omh7BXholnqR6igAiYjLMAyDX/alM3/tIeJ3p3J6abfIQG+GXBDB4O5h9G3TSBYVtZTAkQ2ll7a+h4y9ZZ8P61EaeoZCRB9d1hKpJQUgEWnwcgtL+HzjEeavO1hmkdHLO4YwZkAMV3cJxezWCAbxFubA/pW2wLPnBzh14sxzbh4Qc5mtl6fzENtkgyJyzhSARKTB2n88l/+uO8TijUfILbTNVePnaebWvq24a0AMHUJd/O4tw4Csw7awk/C9bTDz2etoeQfZxvF0GgIdBoJ3oNNKFWlsFIBEpEGxWA1WJaQxb+1Bft6bbt/frqUfYwbE8JcLo/D3drHBzJZi21IS6XtKv/bC8QTb98Kssm2D25X28gyF6IvBrH+mReqD/rJEpEHIyi/ms98P89/1h0g6kQ/YpqYZ2CWUMZfEcGn7ENwa+mWugizbYGV70Cn9OnGg4tmWwbaeVquLbIGn83W21dE1J49IvVMAEhGn2nUsmw/XHeTLzUcpKLbd2RTo48HI/tHcdXEbooN9nVzhnxgGZB/9U09O6XZuSuWv8/C1hZuQTmW/gtuBRyOZk0jEhSgAiYjDlVisLNuZyry1B9mQeGagb5dwf+6+JIZhvaPw8WwAt68XF0Diaji2FdJPB519UJxX+WuahduCTsvOpSGnNPT4R+pOLZEGRAFIRBwmPbeQBRuS+PjXJPsszWY3E0O6hzN6QBsuahvs/HW4igtsd2Lt+NI2MLkop3wbN3fbyujlenQ6aKCyiItQABKRevfH4Uzmrz3I/7Yeo8hiu8zVws+Tv8a25q+xrYkIdPLEfVWFHv9IaHcVtDwr6DSPAbOLDcQWkTIUgESk3mw5nMmbP+xmzb4M+75erQIZc0kM1/eMwMvdiZe5qgs93YdDt+HQqr8uXYk0QgpAIlLnElJyeGtZAst2pgLgYTZxQ89IxlwSQ+/oIOcVptAjIqUUgESkziRl5DN9xR6+3HIUwwA3E/zlwlY8NrCj8+7mUugRkQooAInIeUvLLuCdlftY8FsSxRbbAl1DLwhn4qBOdAzzd3xBCj0iUg0FIBE5Z5n5RcxcfYB5axPtc/hc3jGEpwd3pmerIMcWo9AjIrXQIP4VeO+994iJicHb25vY2Fg2bNhQadurrroKk8lU7uv666+3tzEMg8mTJxMREYGPjw9xcXHs3bu30mOKSO3kFZbw7sq9XP7Gj8xcvZ+CYisXtg7i03EX8997Yx0XfgwD9sXD5+PgzQ6w4HbY9pkt/PhHwsUPwT3L4IkdMGQqtI5V+BERoAH0AC1cuJCJEycyc+ZMYmNjmT59OoMHDyYhIYHQ0NBy7b/44guKis4sFpiRkUGvXr247bbb7PveeOMN3n77bebPn0/btm154YUXGDx4MDt37sTbWzOuipyrwhILn/yaxHs/7iM91/Z32CXcn6cHd+aaLqGOm8On+BRsXQjrZ8Dx3Wf2q6dHRGrIZBiG4cwCYmNj6d+/P++++y4AVquV6OhoHnnkEZ577rlqXz99+nQmT57MsWPH8PPzwzAMIiMjefLJJ3nqqacAyMrKIiwsjHnz5jFq1KhyxygsLKSwsND+ODs7m+joaLKysggICKijMxVxXSUWK19sOsr/i9/L0cxTAMS08OWJQZ24sWek49boykmB3z6A3+dAfumt9Z7NoPdf4YJbFXpEmrjs7GwCAwNr9Pnt1B6goqIiNm7cyKRJk+z73NzciIuLY926dTU6xuzZsxk1ahR+fn4AJCYmkpKSQlxcnL1NYGAgsbGxrFu3rsIANHXqVF566aXzPBuRxsdqNfh+ewpvLU/gwHHb8g/hAd48OrAjt/VrhYfZQWHj2FZY/3+wbTFYi237AltD7ANw4V2afVlEas2pASg9PR2LxUJYWFiZ/WFhYezevbuSV52xYcMGtm/fzuzZs+37UlJS7Mf48zFPP/dnkyZNYuLEifbHp3uARJoqwzBYvec4/1qWwPaj2QA09/Xgoas6cNeANnh7OGACQ6sV9iy1BZ+DP5/ZHx1rG9vT5QYwO/0qvoi4KJf+12P27Nn06NGDiy666LyO4+XlhZeXVx1VJeLafj94gjd+SLAvUurnaea+y9tx3+Vt8fd2wPIPhbmw5RP4dQacOGDbZzJD95ttwadV3/qvQUQaPacGoJCQEMxmM6mpqWX2p6amEh4eXuVr8/LyWLBgAS+//HKZ/adfl5qaSkRERJlj9u7du24KF2mEdiRn8dayPazcnQaAp7sbYwa0YfxVHQj286z/AjIPw4b3YeOHUJhl2+cdCH3HwkX3Q2BU/dcgIk2GUwOQp6cnffv2JT4+nuHDhwO2QdDx8fE8/PDDVb520aJFFBYWcuedd5bZ37ZtW8LDw4mPj7cHnuzsbH799VfGjx9fH6ch4tKO5xTyyv928s0fyYBtdfYR/aJ5dGAHxyxSevg3WP8e7PwGDIttX3B7uHi8bXCzp1/91yAiTY7TL4FNnDiRMWPG0K9fPy666CKmT59OXl4eY8eOBWD06NFERUUxderUMq+bPXs2w4cPp0WLFmX2m0wmHn/8cV599VU6duxovw0+MjLSHrJExOaXvek8vnAL6bm2uyBv6hXJE4M60TaknkOHpQR2fWMb33PktzP7214BF0+Ajtfqbi4RqVdOD0AjR47k+PHjTJ48mZSUFHr37s3SpUvtg5iTkpJw+9M/hAkJCfzyyy8sW7aswmM+88wz5OXlcf/995OZmclll13G0qVLNQeQSKkSi5XpK/by3qp9GAZ0CmvGtBG9uSCqnu+mOpUJmz6EDf+BrMO2fWZP6HGbrccnvEf9/nwRkVJOnweoIarNPAIiriY58xSPLdjMbwdPAnD7RdFMvqE7Pp71eGfXiQOwfiZs/giKbbfT4xsC/e+D/vdCs/KTnoqI1JbLzAMkIo61YmcqTy3+g8z8Ypp5uTP1Lz24sVdk/fywonzYvQT++AT2/wiU/r9WaDfb3Vw9bgMP9cqKiHMoAIk0AUUlVv75/W7mrEkEoEdUIO/+tQ9tWtTxWB/DgKT1ttCz/cuyC5J2vNYWfNpdBY5aMkNEpBIKQCKN3KGMPB75dDNbj9huLb/n0rY8O7QzXu51eMkrMwn+WGCbv+dk4pn9QW2g1+3QaxQEt627nycicp4UgEQasW//SGbSF9vILSwhyNeDf93ai7huYdW/sCYKc213cm35pOxMzZ7NbIuR9v4rtB6gu7lEpEFSABJphAqKLbz07U4+3ZAEQL82zXn79j5EBp3nvD5WKxz6xRZ6dn5zZkAzJtst7L3/Cl1v1Nw9ItLgKQCJNDJ7U3N4+JPNJKTmYDLBhKs68HhcR9zPZ+HSjP22S1x/LICspDP7g9tD79uh5ygI0vp5IuI6FIBEGgnDMFj0+xGmfLODU8UWQpp5MX1kby7rGHJuByzIgh1f2Xp7Dq8/s98rEC64GXr9FaIv0oBmEXFJCkAijUBuYQnPf7mNr7bYlrO4vGMI00b0pqV/LRf5tVrgwCpb6Nn9PygpsO03uUH7a2yXuDpfBx4OWCJDRKQeKQCJuLjtR7N45NPNJKbnYXYzMXFQJ8Zf2R43t1r0zBzfY7t1/Y+FkJN8Zn/LLra7uHqOhICIyl8vIuJiFIBEXJRhGHy47hD/WLKLIouVyEBv3r69D/1igmt2gOIC2Pk1bJwLSevO7PdpDhfcauvtieyjS1wi0igpAIm4oKz8Yp75/A9+2JEKQFzXMP51W0+CfD2rf/HxPbBxnq3H55RtOQxMZug4yBZ6Og0B91peOhMRcTEKQCIuZuOhkzz66WaOZp7C0+zGpOu6cPclMZiq6qkpKYRd38Lvc223sZ8WGA0XjoY+d+kSl4g0KQpAIi7CajX4z88HePOHBCxWgzYtfHn39gvp0aqKFdzT99kucW35BE6dsO0zudl6efqOhQ4Dwa0eF0EVEWmgFIBEXEB6biETP/uDn/YcB+DGXpG8dvMF+Ht7lG9cUgS7S3t7zp6hOSDqTG9PYJSDKhcRaZgUgEQauEMZeYz6z3qOZRXg7eHGizd2Z2T/6PKXvDL2w6b5sPljyE+37TO5QYdB0G+s7btZf/IiIqAAJNKgHc08xV9n/cqxrALatfRj5p196RTmf6ZBSREkLLENaj6w6sx+/whbT8+FozVDs4hIBRSARBqotJwC7vzgV45mnqJtiB8L7r+YUH9v25MnEs/09uSllb7CBB3ioO/dtjE+6u0REamU/oUUaYBO5hVx1wcbSEzPIyrIh4/viyXU12xbgHTjXNi/8kzjZmFnenuat3Fe0SIiLkQBSKSByS4oZvScDSSk5hAW4MUn9/UncvtMWD8TclPONGx/je1Ors5DwVzBYGgREamUApBIA5JfVMLYub+x7WgWwX6efHxPP9qseQ42f2Rr4NcS+twJF46B4LbOLVZExIUpAIk0EAXFFsZ9+DsbD50kwNud/47tQ4dfnoTti213c133JvQZDe41mO1ZRESqpAAk0gAUlVh56ONNrNmXgZ+nmQ/H9KL7L4/ZVmR3c4dbZkP34c4uU0Sk0VAAEnGyEouVxxduZuXuNLzc3ZhzZw96r5kA+1aA2QtGfAidhzi7TBGRRkUBSMSJrFaDZz7fynfbUvAwm/hgVBdi1z5gm8HZ3Qdu/xTaX+3sMkVEGh0FIBEnMQyDF77ezhebjmJ2MzHzto5c/usDcPhX8PSHOz6DNpc4u0wRkUZJAUjECQzD4LXvdvHxr0mYTPDO8DYM/PU+OLYFvAPhzi+hVV9nlyki0mgpAIk4wfQVe5n1cyIA/74+gut+vw/SdoJvC7jrK4jo6dwCRUQaOQUgEQd7f/V+/l/8XgBeH9SC4ZvHQcY+aBYOo7+G0C5OrlBEpPFTABJxoA/XHWTq97sBePmKZozcdj9kHoLAaFv4adHeyRWKiDQNCkAiDrLo98NM/noHAC9c7MHo3eMh+yg0bwtjvoGg1k6uUESk6VAAEnGAb/9I5tnPtwLwzIUW7tn7uG0V95BOMPobCIhwboEiIk2MApBIPVuxM5UnFm7BasCTF+QzPvEZTKdOQFgPuOtLaNbS2SWKiDQ5CkAi9ejnvcd56ONNlFgNHut8kocPT8JUmA2RF8Kdn4NvsLNLFBFpktycXYBIY7Uh8QTjPvydIouVh9ul8PixZ23hp/UA24BnhR8REadRD5BIPfjjcCb3zPuNgmIrE6IP8mTay5hKCqDtlbblLTz9nF2iiEiTpgAkUsd2Hctm9JwN5BaWMCEigadOTMVkKYKOg20Lm3p4O7tEEZEmTwFIpA7tP57LXbN/JetUMQ+H/sGTWW9hspZA15vgltng7unsEkVEBAUgkTpz+EQ+d8z6lfTcIh4J/o2JOf8Pk2GFHiNg+Aww689NRKSh0L/IInXgWNYp/vrBelKyC3gi6Ccey59pe+LC0XDDdHAzO7U+EREpSwFI5DzlFpZw95zfOHziFE8HLGdCwVzbE7EPwpB/gsnk3AJFRKQcBSCR82C1GjyxcAsJqTk86/st44s+tT1x2RMwcIrCj4hIA6UAJHIepi3fw6qdR5ni+Sljrd/bdl79d7jiaYUfEZEGTAFI5Bx9+0cy3676hcWe79LL7YBt56BX4NJHnVuYiIhUSwFI5BxsO5LFT5+/yxLP2TQzFYB3EAx7F7re6OzSRESkBhSARGrpeEY6R+bcw5tuqwEwWl+C6ZZZENjKyZWJiEhNKQCJ1EJR0m+UzL+LodZjWHCj+PJn8L76Gd3mLiLiYhSARGrCasVY+w7mFS8RgYVkQjD+8gFRPa92dmUiInIOFIBEqpOTAl8+iOnAj5iBJZZYmo/8Py65oIOzKxMRkXOkACRSlT3L4KvxkJ/OKcOTF0vG0GnIQ1x/QTtnVyYiIudBAUikIiWFsOJFWP9/AOymDROKHubCC2O557K2zq1NRETOmwKQyJ+l74XFYyFlGwCfe9zA33JupXvrUF69+QJMmuBQRMTlKQCJnGYYsPkj+P4ZKM7H8Anm382e4O3D7YkI9GbmXX3xctfdXiIijYECkAhAQRZ8+zjs+ML2uO0VvBP4NG+vz8Hbw41Zo/sR6u/t1BJFRKTuKACJHN4An98LmUlgMsM1z7PY5xamLd4OwL9u68UFUYFOLlJEROqSApA0XVYL/DINfpwKhgWC2sCtc9hkbc/f3l8PwCPXdOCGnpFOLlREROqaApA0TdnJ8MX9cPBn2+MLboUbpnGs0JMH3l1DkcXKoG5hPBHXybl1iohIvVAAkqZn93fw9UNw6iR4+MH1/4Jet1NQYuX+D9dxPKeQzmH+/Htkb9zcdMeXiEhjpAAkTUfxKVj2Avw2y/Y4ohfcMgdCOmAYBs8s3sq2o1k09/XggzH9aOalPw8RkcZK/8JL03AiERbcAWk7bI8HPAwDp4C7JwAzVu/nmz+ScXcz8X939CU62NeJxYqISH1zc3YB7733HjExMXh7exMbG8uGDRuqbJ+ZmcmECROIiIjAy8uLTp068d1339mff/HFFzGZTGW+unTpUt+nIQ1ZUR58erst/Pi1hDs/h8H/sIefFTtTefOHBABevKk7A9q3cGa1IiLiAE7tAVq4cCETJ05k5syZxMbGMn36dAYPHkxCQgKhoaHl2hcVFTFo0CBCQ0NZvHgxUVFRHDp0iKCgoDLtunfvzooVK+yP3d3V0dVkGQZ8+xgc3wXNwuD+VRBw5q6uPak5PLZgM4YBd17cmjsvbuO8WkVExGGcmgymTZvGuHHjGDt2LAAzZ85kyZIlzJkzh+eee65c+zlz5nDixAnWrl2Lh4cHADExMeXaubu7Ex4eXuM6CgsLKSwstD/Ozs6u5ZlIg/XbB7BtkW1+n1vnlgk/J/OKuG/+7+QVWbi4XTBTbuzuxEJFRMSRnHYJrKioiI0bNxIXF3emGDc34uLiWLduXYWv+eabbxgwYAATJkwgLCyMCy64gNdeew2LxVKm3d69e4mMjKRdu3bccccdJCUlVVnL1KlTCQwMtH9FR0ef/wmK8x3ZCEsn2bYHvQQxl9qfKrZYeejjTSSdyKdVcx/+746+eJidfkVYREQcxGn/4qenp2OxWAgLCyuzPywsjJSUlApfc+DAARYvXozFYuG7777jhRde4K233uLVV1+1t4mNjWXevHksXbqUGTNmkJiYyOWXX05OTk6ltUyaNImsrCz71+HDh+vmJMV58jLgs9FgLYauN9oGPZ/l1f/tZN2BDHw9zXwwph/Bfp5OKlRERJzBpQbHWK1WQkND+c9//oPZbKZv374cPXqUN998kylTpgAwdOhQe/uePXsSGxtLmzZt+Oyzz7j33nsrPK6XlxdeXl4OOQdxAKsFvrgPso9AcHsY9n9w1grun/yaxPx1hwD498jedAkPcFalIiLiJE4LQCEhIZjNZlJTU8vsT01NrXT8TkREBB4eHpjNZ1bk7tq1KykpKRQVFeHpWf7/4oOCgujUqRP79u2r2xOQhmv167B/JXj4wsiPwPtMwPn1QAaTv7at8fXkoE4M7l7zsWIiItJ4OO0SmKenJ3379iU+Pt6+z2q1Eh8fz4ABAyp8zaWXXsq+ffuwWq32fXv27CEiIqLC8AOQm5vL/v37iYiIqNsTkIZpzzJbAAK4YTqEdbM/dfhEPuM/3kSJ1eCGnhE8fE0H59QoIiJO59RRnxMnTmTWrFnMnz+fXbt2MX78ePLy8ux3hY0ePZpJkybZ248fP54TJ07w2GOPsWfPHpYsWcJrr73GhAkT7G2eeuopVq9ezcGDB1m7di0333wzZrOZ22+/3eHnJw528hB8Mc623e9e6DXS/lReYQnjPvydE3lFdI8M4M1be2EyaZkLEZGmyqljgEaOHMnx48eZPHkyKSkp9O7dm6VLl9oHRiclJeHmdiajRUdH88MPP/DEE0/Qs2dPoqKieOyxx3j22WftbY4cOcLtt99ORkYGLVu25LLLLmP9+vW0bNnS4ecnDlRcYBv0XJAJkRfCkKllnv7Hd7vYnZJDSDNPZo3uh4+nueLjiIhIk2AyDMNwdhENTXZ2NoGBgWRlZREQoAGyLuHbx2HjXPAJhgd+gqAzUxlsPHSCW2bYplb4ZFwsl7QPcVKRIiJSn2rz+a2JT8T1bfnEFn4wwS2zyoSfYouVv31hG/R8W99WCj8iIgIoAImrS9kG/3vCtn3VJOgQV+bp2b8kkpCaQ3NfDyZd19UJBYqISEOkACSu61QmLLwLSgpsweeKp8s8ffhEPtNX7AHg79d302SHIiJipwAkrskw4OsJcDIRAqPhL7PgrAHzhmEw+evtFBRbubhdMLdcGOXEYkVEpKFRABLXtPZt2P0/MHvCiPngG1zm6e+3p/BjwnE8zCZeHd5Dt7yLiEgZCkDiehJ/hhUv2raHvg5Rfcs8nVNQzEvf7gBg/JXt6RDazMEFiohIQ6cAJK4l+xgsvgcMK/S6HfqOLdfkrWV7SM0uJKaFLw9drdmeRUSkPAUgcR2WYlg8FvLSILQ7XD+tzCKnAFuPZDJ/3UEAXh3eA28PTXgoIiLlKQCJ61jxIiStA68AGPlf8PQt83SJxcrfvtyGYcCw3pFc1lFz/oiISMUUgMQ17PgK1r1r2x7+f9CifbkmH647xPaj2QR4u/P89d3KPS8iInKaApA0fOl74euHbduXPApdbyzX5FjWKd5algDAs0O70NLfy5EVioiIi1EAkoatKM822WFRDrS5FAZOqbDZi9/sIK/IwoWtg7i9f2sHFykiIq5GAUgaLsOAbx+D47ugWRjcOhfM7uWardiZyg87UnF3M/HaX3rg5qY5f0REpGoKQNJw/fYBbFsEJjPcNg/8w8o1yS8qYco3tjl/7r28LV3Cq179V0REBBSApKE68jssnWTbHvQStLmkwmbTV+zlaOYpooJ8eGxgRwcWKCIirkwBSBqevHT4bDRYi20Dngc8XGGzncnZzP4lEYBXhnfH17P85TEREZGKKABJw2K1wOf3QfZRCG4Pw/6v3GSHAFarwd+/2obFajD0gnCu6VL+8piIiEhlFICkYVn9Ohz4ETx8YeRH4F3xmJ5PNiSxOSmTZl7uTLmxu4OLFBERV6cAJA3HnmW2AARw4/+DsIonM0zLKeD1pbsBePLaToQHejuqQhERaSRqHYDmzp3LokWLyu1ftGgR8+fPr5OipAnKToYvxtm2+98HPUdU2vSV/+0ip6CEHlGBjB4Q45j6RESkUal1AJo6dSohIeXXWAoNDeW1116rk6KkCVrxEhRkQkRvGFz579FPe47z7R/JuJngtZt7YNacPyIicg5qHYCSkpJo27Ztuf1t2rQhKSmpToqSJuboRti6wLZ9wzRwr3gZi4JiC89/tR2A0QNi6NEq0FEViohII1PrABQaGsrWrVvL7f/jjz9o0aJFnRQlTYhhwNK/2bZ7joKovpU2fXflPpJO5BMe4M2T13ZyUIEiItIY1ToA3X777Tz66KP8+OOPWCwWLBYLK1eu5LHHHmPUqFH1UaM0Zju/gsPrwd0HBk6utNm+tBze/2k/AC/e1A1/bw8HFSgiIo1RrWeOe+WVVzh48CADBw7E3d32cqvVyujRozUGSGqnuACWl4aeSx+DwKgKmxmGwd++3E6xxWBgl1AGdw93YJEiItIY1ToAeXp6snDhQl599VW2bNmCj48PPXr0oE2bNvVRnzRmv86AzCTwj4BLH6202aKNR9iQeAIfDzMvDeuOqYKJEUVERGrjnNcO6NixIx07au0lOUe5afDTW7btgVPA06/CZifyipj63S4AHo/rSKvmvo6qUEREGrFajwG65ZZbeP3118vtf+ONN7jtttvqpChpAn78BxTlQGQf6Dmy0mb/WLKLk/nFdAn3557Lyt99KCIici5qHYB++uknrrvuunL7hw4dyk8//VQnRUkjl7IdNn1o2x78GrhV/Gu4bn8Gn286gskE/7i5Bx5mTVwuIiJ1o9afKLm5uXh6epbb7+HhQXZ2dp0UJY2YYcAPfwPDCt2GQZtLKmxWWGLh719tA+D2i1rTt01zR1YpIiKNXK0DUI8ePVi4cGG5/QsWLKBbt4rXbhKx27MUEleD2RMGvVxps/dXH+DA8TxCmnny7OAuDixQRESagloPgn7hhRf4y1/+wv79+7nmmmsAiI+P55NPPmHx4sV1XqA0IiVFsOx52/bFD0HzmAqbJabn8e6P+wB44YZuBPpqzh8REalbtQ5AN954I1999RWvvfYaixcvxsfHh169erFy5UqCg4Pro0ZpLH6fDRn7wK8lXP5khU0Mw+CFr7ZTVGLlsg4h3NQr0sFFiohIU3BOt8Fff/31XH/99QBkZ2fz6aef8tRTT7Fx40YsFkudFiiNRP4JWPVP2/bVfwfvgAqbffNHMr/sS8fT3Y1Xh1+gOX9ERKRenPNtNT/99BNjxowhMjKSt956i2uuuYb169fXZW3SmKx+3bbae2h3uHB0hU2y8ot55X87AXjk6g7EhFQ8N5CIiMj5qlUPUEpKCvPmzWP27NlkZ2czYsQICgsL+eqrrzQAWiqXvhd++8C2Pfgf4GausNnrP+wmPbeI9i39uP/Kdg4sUEREmpoa9wDdeOONdO7cma1btzJ9+nSSk5N555136rM2aSyWPQ/WEug0BNpfXWGTrUcy+eTXJMA254+Xe8UhSUREpC7UuAfo+++/59FHH2X8+PFaAkNqbv+Ptlvf3dzh2lcrbTZt+R4AhveO5OJ2LRxVnYiINFE17gH65ZdfyMnJoW/fvsTGxvLuu++Snp5en7WJq7OU2CY9BOh/H4RUHJw3HjrJqoTjmN1MPB7XyYEFiohIU1XjAHTxxRcza9Ysjh07xgMPPMCCBQuIjIzEarWyfPlycnJy6rNOcUWbP4S0neAdBFc+W2mz6StsvT+3XBilgc8iIuIQtb4LzM/Pj3vuuYdffvmFbdu28eSTT/LPf/6T0NBQbrrppvqoUVxRQRas/Idt+6rnwLfiOaJ+O3iCn/em4+5m4pFrdGlVREQc47xWl+zcuTNvvPEGR44c4dNPP62rmqQx+PktyE+HFh1sl78q8e/SsT+39WtFdLCvo6oTEZEmrk6W1zabzQwfPpxvvvmmLg4nru5EIqyfYdu+9h9grngpi3X7M1i7PwMPs4kJV3dwYIEiItLU1UkAEiljxRSwFEG7q6DT4AqbGIbBv0vH/ozsH02r5ur9ERERx1EAkrp1aC3s/BpMbjD4NahkKYu1+zPYkHgCT7Oben9ERMThFICk7litsHSSbfvC0RDWvcJmhmHY5/35a2xrIgJ9HFWhiIgIoAAkdWnrQji2BTz9bQueVuKnvelsPHQSL3c3xl/V3nH1iYiIlFIAkrpRlAfxL9m2r3gSmoVW2Ozs3p87L25DWIC3oyoUERGxUwCSurHmbcg5BkGtIXZ8pc1+TEjjj8OZeHu48eCV6v0RERHnUACS85d1FNb8P9v2oJfBo+JenbN7f8YMiKGlv5ejKhQRESlDAUjOX/xLUHIKWg+AbsMrbbZ8Zyrbj2bj62nm/ivaOa4+ERGRP1EAkvNzZKNt8DPA4H9Uetu71Wrw7xV7Abj7khhaNFPvj4iIOI8CkJw7wziz2nvPURDVt9Kmy3amsOtYNs283Bl3uXp/RETEuRSA5Nzt+BIOrwd3Hxg4udJmVqvBv5fben/uuTSG5n6ejqpQRESkQgpAcm6KC2xLXgBc9jgERlXa9Lvtx0hIzcHf2517L1Pvj4iIOJ8CkJyb9f8HmUngHwmXPFJpM4vVYHrp2J/7LmtHoG/FC6OKiIg4kgKQ1F5uGvw8zbYdNwU8/Spt+r+tyexLyyXA252xl8U4pj4REZFqKABJ7a18FYpyILIP9BhRabMSi5X/V9r7c/8V7QjwVu+PiIg0DE4PQO+99x4xMTF4e3sTGxvLhg0bqmyfmZnJhAkTiIiIwMvLi06dOvHdd9+d1zGlFlK2w+b/2rYHTwW3yn+Fvt6SzIH0PIJ8Pbj70rYOKlBERKR6Tg1ACxcuZOLEiUyZMoVNmzbRq1cvBg8eTFpaWoXti4qKGDRoEAcPHmTx4sUkJCQwa9YsoqKizvmYUgunb3s3rLYJD9sMqLRpscXK2yttvT8PXNGeZl7uDipSRESkeibDMAxn/fDY2Fj69+/Pu+++C4DVaiU6OppHHnmE5557rlz7mTNn8uabb7J79248PCq+nFLbY1YkOzubwMBAsrKyCAgIOMeza4QSvodPR4HZEx7+DZrHVNr0s98O88znW2nh58lPz1yNnwKQiIjUs9p8fjutB6ioqIiNGzcSFxd3phg3N+Li4li3bl2Fr/nmm28YMGAAEyZMICwsjAsuuIDXXnsNi8VyzscEKCwsJDs7u8yX/ImlGJY9b9u++KEqw09RyZnenwevbK/wIyIiDY7TAlB6ejoWi4WwsLAy+8PCwkhJSanwNQcOHGDx4sVYLBa+++47XnjhBd566y1effXVcz4mwNSpUwkMDLR/RUdHn+fZNUJbPoGMfeDbAi5/ssqmn286wpGTpwhp5sWdF7dxUIEiIiI15/RB0LVhtVoJDQ3lP//5D3379mXkyJH8/e9/Z+bMmed13EmTJpGVlWX/Onz4cB1V3EgUF8Dq123blz8F3pV3KxaWWHh35T4AHrqqPT6eZkdUKCIiUitOuzYREhKC2WwmNTW1zP7U1FTCw8MrfE1ERAQeHh6YzWc+VLt27UpKSgpFRUXndEwALy8vvLy0OGelfp8D2UchIAr63VNl089+P8LRzFOEBXjx19jWDipQRESkdpzWA+Tp6Unfvn2Jj4+377NarcTHxzNgQMV3F1166aXs27cPq9Vq37dnzx4iIiLw9PQ8p2NKNQpz4Oe3bNtXPgse3pU2LSi28F5p78+Eqzvg7aHeHxERaZicegls4sSJzJo1i/nz57Nr1y7Gjx9PXl4eY8eOBWD06NFMmjTJ3n78+PGcOHGCxx57jD179rBkyRJee+01JkyYUONjSi2tnwn56RDcHnr/tcqmCzYkkZJdQESgNyP7axyViIg0XE69PWfkyJEcP36cyZMnk5KSQu/evVm6dKl9EHNSUhJuZ020Fx0dzQ8//MATTzxBz549iYqK4rHHHuPZZ5+t8TGlFvJPwNq3bdtX/w3Mlc/kXFBs4b1V+wF4+JoOeLmr90dERBoup84D1FBpHqBSy6fAmukQdgE88HOVsz5/8PMBXl2yi6ggH3586io83V1qfL2IiDQCLjEPkDRwOSnw6/u27WuerzL85BeVMHO1rffn0YEdFH5ERKTB0yeVVOynf0HJKWjVHzoNqbLpf9cdIj23iNbBvvzlwlYOKlBEROTcKQBJeScPwcZ5tu2Bk8FkqrRpbuHZvT8d8TDrV0pERBo+fVpJeatfB2sxtLsK2l5RZdP5aw9yMr+YtiF+DO8d6Zj6REREzpMCkJR1PAH++NS2fc3kKpvmFBTzn58OALaxP+7q/RERERehTywp68d/gGGFLjdAq75VNp235iBZp4pp19KPm3pFOahAERGR86cAJGckb4adXwMmuPrvVTbNOlXMrJ9tvT+Px3XC7Fb5OCEREZGGRgFIzlj5qu17zxEQ1q3KpnN+SSS7oIROYc24vkeEA4oTERGpOwpAYnNwDexbAW7ucNVzVTbNzC9izi+JgHp/RETENSkACRgGrHzFtn3haAhuV2XzD35OJKewhC7h/gzpHu6AAkVEROqWApDYen6S1oG7N1zxdJVNT+QVMXeNrffniUGdcFPvj4iIuCAFoKbOaoX4l23bF42DgKrn8vnPTwfIK7LQPTKAa7tpgVkREXFNCkBN3a6vIWUrePrDpU9U2TQ9t5D5aw8CMHFQJ0xVzBAtIiLSkCkANWWWElj5D9v2JQ+DX4sqm7+/ej+nii30ahXINV1CHVCgiIhI/VAAasq2LoCMveATDBc/VGXTjNxC/rv+EGAb+6PeHxERcWUKQE1VSSGset22fdkT4B1QZfP5aw9SUGylZ6tAruzU0gEFioiI1B8FoKZq43zISgL/CNvg5yrkFpYwf52t92f8le3V+yMiIi5PAagpKsqDn960bV/xNHj4VNl8wYYk25pfIX5cq3l/RESkEVAAaop+fR/y0qB5DPS5q8qmRSVWPvjZNu/PA1e206zPIiLSKCgANTWnMmHNdNv2VX8Dd88qm3+15Sgp2QWEBXgxvI9WfBcRkcZBAaipWfsOFGRBy67Q49Yqm1qtBjNX7wfg3sva4uVudkSFIiIi9U4BqCnJTYP1M2zb1zwPblUHmmU7UzlwPI8Ab3duv6i1AwoUERFxDAWgpuTnaVCcB5EXQpfrq2xqGAYzSnt/Rg+Iwd/bwxEVioiIOIQCUFOReRh+n23bHjgZqrmVff2BE/xxOBMvdzfuvjSm/usTERFxIAWgpmL162ApgpjLod1V1TY/3fszol80Ic286rk4ERERx1IAagrS98KWT2zbNej92ZGcxU97jmN2M3H/Fe0cUKCIiIhjKQA1BT++BoYFOg2F6IuqbT5z9QEAru8RQXSwb31XJyIi4nAKQI1dyjbY8YVt+5rnq21+KCOPJVuTAXjwyvb1WZmIiIjTKAA1ditftX2/4FYIv6Da5v/56QBWA67q3JJukVUvkCoiIuKqFIAas6RfYc9SMJnh6r9V2/x4TiGLNh4BbIueioiINFYKQI2VYUD8y7btPndCi+oDzdw1iRSVWOnTOoiL2gbXc4EiIiLOowDUWB34EQ79AmZPuPKZapvnFBTz3/WHAFvvj6maO8VERERcmQJQY3R270//+yCwVbUv+eTXJHIKSugQ2oy4rmH1XKCIiIhzKQA1Rrv/B8mbwcMPLptYbfOCYgsf/JIIwANXtMPNTb0/IiLSuCkANTZWy5k7vwY8BM1aVvuSLzcf5XhOIRGB3gzrHVXPBYqIiDifAlBjs20RHN8N3kEw4OFqm1usBu+XLntx3+Xt8HTXr4SIiDR++rRrTEqKbLM+A1z2OPgEVfuSH3akcDAjn0AfD0b1j67X8kRERBoKBaDGZPOHkHkImoXBRfdX29wwDGassvX+jLkkBj8v9/quUEREpEFQAGosrFZY+45t+/KnwNOv2pes2ZfBtqNZeHu4cfclMfVbn4iISAOiANRYHPgRTh4Er0DbxIc1MLN07M+o/q0J9vOsx+JEREQaFgWgxuL3ObbvvW8Hz+pXcN96JJNf9qVjdjNx3+Vt67k4ERGRhkUBqDHIToaE723bfcfW6CWne3+G9YqkVfPqA5OIiEhjogDUGGz6EAwLtLkUQrtU2/zA8Vy+354CwANa9FRERJogBSBXZymBjfNt2/3uqdFLZv18AMOAgV1C6RzuX4/FiYiINEwKQK5u7w+Qkwy+LaDrjdU2T80u4PONRwEYf5V6f0REpGlSAHJ1pwc/97kT3L2qbT7nl0SKLFb6xzSnX0xwPRcnIiLSMCkAubITibAv3rbd9+5qm2edKubjX5MA9f6IiEjTpgDkyjbNBwxofw0Et6u2+UfrD5FbWELnMH+u7hxa//WJiIg0UApArqqkCDb917Zdg8HPBcUW5q5JBODBq9phMpnqszoREZEGTQHIVe3+FvLTwT8COg2ptvmijUdIzy0iKsiHG3pGOqBAERGRhksByFX9Ptf2/cLRYPaosmmJxcqsnw4AMO7ytniY9baLiEjTpk9CV3R8Dxz8GUxutgBUje+2p5B0Ip9gP09G9m/tgAJFREQaNgUgV7SxtPen0xAIbFVlU8MwmLHKtuzF3ZfE4ONpru/qREREGjwFIFdTfAq2fGzbrsHg55/2prPrWDa+nmZGD2hTz8WJiIi4BgUgV7PjSyjIgqDWttvfqzFj1T4Abr+oNUG+nvVdnYiIiEtQAHI1p2d+7ns3uFV9OWtT0knWHziBh9nEfZe3rf/aREREXIQCkCs5thWO/AZuHtDnrmqbzywd+zO8dxQRgT71XZ2IiIjLUAByJacHP3e9EZpVPZPzvrQclu1MBeCBK6ufJVpERKQpUQByFYU5sPUz23YNBj+/v9o278+13cLoEOpfn5WJiIi4nAYRgN577z1iYmLw9vYmNjaWDRs2VNp23rx5mEymMl/e3t5l2tx9993l2gwZUv1syQ3atkVQlAstOkLMZVU2PZZ1iq+2HAXgQS16KiIiUo67swtYuHAhEydOZObMmcTGxjJ9+nQGDx5MQkICoaEVX+YJCAggISHB/riida2GDBnC3Llz7Y+9vLzqvnhHMQz4rXTwc797oJp1vGb/nEixxSC2bTAXtm7ugAJFRERci9N7gKZNm8a4ceMYO3Ys3bp1Y+bMmfj6+jJnzpxKX2MymQgPD7d/hYWFlWvj5eVVpk3z5i4cBI5uhNRt4O4NvUZV2TQzv4hPNiQBMF69PyIiIhVyagAqKipi48aNxMXF2fe5ubkRFxfHunXrKn1dbm4ubdq0ITo6mmHDhrFjx45ybVatWkVoaCidO3dm/PjxZGRkVHq8wsJCsrOzy3w1KKdvfe/+F/ANrrLph+sOkV9koWtEAFd2aumA4kRERFyPUwNQeno6FoulXA9OWFgYKSkpFb6mc+fOzJkzh6+//pqPPvoIq9XKJZdcwpEjR+xthgwZwocffkh8fDyvv/46q1evZujQoVgslgqPOXXqVAIDA+1f0dHRdXeS5+vUSdj+uW27msHPp4oszFt7ELD1/lR0aVBEREQawBig2howYAADBgywP77kkkvo2rUr77//Pq+88goAo0aduUzUo0cPevbsSfv27Vm1ahUDBw4sd8xJkyYxceJE++Ps7OyGE4K2fAolBRDWA1r1q7LpZ78f5kReEdHBPlx3QbiDChQREXE9Tu0BCgkJwWw2k5qaWmZ/amoq4eE1+wD38PCgT58+7Nu3r9I27dq1IyQkpNI2Xl5eBAQElPlqEAzjzOWvfmOrHPxsGAbzS3t/7r+8He5mpw/vEhERabCc+inp6elJ3759iY+Pt++zWq3Ex8eX6eWpisViYdu2bURERFTa5siRI2RkZFTZpkE6+Atk7AXPZtBzRJVNfz90kgPpefh6mrn5wqpXiBcREWnqnN5NMHHiRGbNmsX8+fPZtWsX48ePJy8vj7FjxwIwevRoJk2aZG//8ssvs2zZMg4cOMCmTZu48847OXToEPfddx9gGyD99NNPs379eg4ePEh8fDzDhg2jQ4cODB482CnneM5O9/70uA28qp7McOFvhwG4oWcEzbxc7sqmiIiIQzn9k3LkyJEcP36cyZMnk5KSQu/evVm6dKl9YHRSUhJubmdy2smTJxk3bhwpKSk0b96cvn37snbtWrp16waA2Wxm69atzJ8/n8zMTCIjI7n22mt55ZVXXGsuoNw02PWtbbvf2Cqb5hQUs2TrMQBG9m8gY5dEREQaMJNhGIazi2hosrOzCQwMJCsry3njgX6eBvEvQVQ/GBdfZdNPNyQx6YtttGvpR/zEK3X3l4iINEm1+fx2+iUwqYDVembh0xqs+/XZ77bLXyP7RSv8iIiI1IACUEO0fyVkJoF3IHS/ucqme1Nz2JyUibubib9o8LOIiEiNKAA1RKcHP/f6K3j6Vtn09ODna7qE0tLfhcY4iYiIOJECUEOTdRT2fG/brmbwc1GJlS8221Z9H9FPg59FRERqSgGoodn0IRhWaHMZtOxcZdP4XamcyCsi1N+Lqzpr3S8REZGaUgBqSCwlsGm+bbua3h84M/j5lr6tNPOziIhILehTsyHZsxRyjoFvCHS9scqmKVkFrN5zHNDlLxERkdpSAGpITg9+7nMnuFc9oHnxxsNYDbgoJpi2IX4OKE5ERKTxUABqKE4kwv7SCQ/73l1lU6vV4LPfjwAwQjM/i4iI1JoCUEOxcZ7te/uBENy2yqa/Jp4g6UQ+zbzcua5HeP3XJiIi0sgoADUEJYWw+SPbdv97q21+evDzjb0i8fV0+nJuIiIiLkcBqCHY9S3kp4N/JHSsesX6rFPFfLfNtvDpiH6a+VlERORcKAA1BL+XrvvVdwyYq+7R+eaPZApLrHQKa0bv6KD6r01ERKQRUgBytuMJcOgXMJnhwtHVNl9UevlrhBY+FREROWcKQM52uven81AIiKyy6a5j2Ww9koWH2cTNfaIcUJyIiEjjpADkTEX58Mcntu0azPx8euHTuK5htGimhU9FRETOlQKQM+34EgqyIKgNtLumyqaFJRa+2lK68Knm/hERETkvCkDO9Pts2/d+Y8Gt6rdi+c5UMvOLiQj05oqOWvhURETkfCgAOUvyFji6Edw8oPed1TY/ffnr1r6tMLtp8LOIiMj5UABylo2lg5+73QTNqu7ROXIyn1/2pQNwW19d/hIRETlfCkDOUJANWxfZtvvdU23zxRuPYBgwoF0LWrfwrefiREREGj8FIGfY9hkU50FIJ2hzaZVNrVaDRaULn47U4GcREZE6oQDkaIYBv82xbfe7B6qZzHDt/gyOZp7C39udIRdo4VMREZG6oADkaEd+g7Qd4O4NvUZV23xh6czPw3pH4u1hru/qREREmgQFIEf7vbT354JbwKd5lU0z84v4YUcKACP7ta7vykRERJoMBSBHyj8B27+wbddg8PNXm49SVGKla0QAF0QF1HNxIiIiTYcCkCP98SlYCiG8B0T1rbb5Z6cHP/drpYVPRURE6pACkCN5B9qWvajB4OftR7PYeSwbT7Mbw3pr4VMREZG65O7sApqUPndCr7+CYam26emZn6/tHkZzP8/6rkxERKRJUQByNDc3qut4Kyg+s/Cp5v4RERGpe7oE1gD9sCOFnIISooJ8uLR9iLPLERERaXQUgBqgsxc+ddPCpyIiInVOAaiBScrIZ+3+DEwmuK1fK2eXIyIi0igpADUwizbaen8u6xBCq+Za+FRERKQ+KAA1IBarweKNtrl/RvTT4GcREZH6ogDUgPy89zjHsgoI9PFgULcwZ5cjIiLSaCkANSCflS58enOfKC18KiIiUo8UgBqIjNxClu9MBXT5S0REpL4pADUQX21Jpthi0CMqkG6RWvhURESkPikANQCGYfBZ6dw/IzTzs4iISL1TAGoA/jiSRUJqDl7ubtzUK9LZ5YiIiDR6CkANwOmZn4deEE6gj4eTqxEREWn8FICcLL+ohG//SAZ0+UtERMRRFICc7PttKeQWltA62JeL27ZwdjkiIiJNggKQky0snfvnNi18KiIi4jAKQE6UmJ7HhsQTuJngVi18KiIi4jAKQE50eubnKzq1JCLQx8nViIiINB0KQE5SYrHyeenCpyM187OIiIhDKQA5yeo9x0nLKSTYz5OBXbXwqYiIiCMpADnJ6bl/bu4Thae73gYRERFH0ievExzPKWTl7jQARmruHxEREYdTAHKCLzcfocRq0Ds6iE5h/s4uR0REpMlRAHIwwzDsl79GaPCziIiIUygAOdimpJPsP56Hj4eZG3tFOLscERGRJkkByMFO9/5c1yMCf28tfCoiIuIMCkAOlFdYwv+2HgM0+FlERMSZFIAcaMnWY+QXWWgb4kf/mObOLkdERKTJUgByoPS8Qnw8zNzWrxUmkxY+FRERcRZ3ZxfQlDx0VQfuuriNs8sQERFp8hpED9B7771HTEwM3t7exMbGsmHDhkrbzps3D5PJVObL29u7TBvDMJg8eTIRERH4+PgQFxfH3r176/s0asTf20ODn0VERJzM6QFo4cKFTJw4kSlTprBp0yZ69erF4MGDSUtLq/Q1AQEBHDt2zP516NChMs+/8cYbvP3228ycOZNff/0VPz8/Bg8eTEFBQX2fjoiIiLgApwegadOmMW7cOMaOHUu3bt2YOXMmvr6+zJkzp9LXmEwmwsPD7V9hYWcWEzUMg+nTp/P8888zbNgwevbsyYcffkhycjJfffWVA85IREREGjqnBqCioiI2btxIXFycfZ+bmxtxcXGsW7eu0tfl5ubSpk0boqOjGTZsGDt27LA/l5iYSEpKSpljBgYGEhsbW+kxCwsLyc7OLvMlIiIijZdTA1B6ejoWi6VMDw5AWFgYKSkpFb6mc+fOzJkzh6+//pqPPvoIq9XKJZdcwpEjRwDsr6vNMadOnUpgYKD9Kzpac/SIiIg0Zk6/BFZbAwYMYPTo0fTu3Zsrr7ySL774gpYtW/L++++f8zEnTZpEVlaW/evw4cN1WLGIiIg0NE4NQCEhIZjNZlJTU8vsT01NJTw8vEbH8PDwoE+fPuzbtw/A/rraHNPLy4uAgIAyXyIiItJ4OTUAeXp60rdvX+Lj4+37rFYr8fHxDBgwoEbHsFgsbNu2jYgI28Kibdu2JTw8vMwxs7Oz+fXXX2t8TBEREWncnD4R4sSJExkzZgz9+vXjoosuYvr06eTl5TF27FgARo8eTVRUFFOnTgXg5Zdf5uKLL6ZDhw5kZmby5ptvcujQIe677z7AdofY448/zquvvkrHjh1p27YtL7zwApGRkQwfPtxZpykiIiINiNMD0MiRIzl+/DiTJ08mJSWF3r17s3TpUvsg5qSkJNzcznRUnTx5knHjxpGSkkLz5s3p27cva9eupVu3bvY2zzzzDHl5edx///1kZmZy2WWXsXTp0nITJoqIiEjTZDIMw3B2EQ1NdnY2gYGBZGVlaTyQiIiIi6jN57fL3QUmIiIicr4UgERERKTJUQASERGRJsfpg6AbotPDorQkhoiIiOs4/bldk+HNCkAVyMnJAdCSGCIiIi4oJyeHwMDAKtvoLrAKWK1WkpOT8ff3x2Qy1emxs7OziY6O5vDhw43yDjOdn+tr7Oeo83N9jf0cdX7nzjAMcnJyiIyMLDOFTkXUA1QBNzc3WrVqVa8/o7EvuaHzc32N/Rx1fq6vsZ+jzu/cVNfzc5oGQYuIiEiTowAkIiIiTY4CkIN5eXkxZcoUvLy8nF1KvdD5ub7Gfo46P9fX2M9R5+cYGgQtIiIiTY56gERERKTJUQASERGRJkcBSERERJocBSARERFpchSA6sF7771HTEwM3t7exMbGsmHDhirbL1q0iC5duuDt7U2PHj347rvvHFRp7UydOpX+/fvj7+9PaGgow4cPJyEhocrXzJs3D5PJVObL29vbQRXXzosvvliu1i5dulT5Gld5706LiYkpd44mk4kJEyZU2L6hv38//fQTN954I5GRkZhMJr766qsyzxuGweTJk4mIiMDHx4e4uDj27t1b7XFr+zdcn6o6x+LiYp599ll69OiBn58fkZGRjB49muTk5CqPeS6/6/Wluvfw7rvvLlfrkCFDqj1uQ3kPqzu/iv4eTSYTb775ZqXHbEjvX00+FwoKCpgwYQItWrSgWbNm3HLLLaSmplZ53HP9260NBaA6tnDhQiZOnMiUKVPYtGkTvXr1YvDgwaSlpVXYfu3atdx+++3ce++9bN68meHDhzN8+HC2b9/u4Mqrt3r1aiZMmMD69etZvnw5xcXFXHvtteTl5VX5uoCAAI4dO2b/OnTokIMqrr3u3buXqfWXX36ptK0rvXen/fbbb2XOb/ny5QDcdtttlb6mIb9/eXl59OrVi/fee6/C59944w3efvttZs6cya+//oqfnx+DBw+moKCg0mPW9m+4vlV1jvn5+WzatIkXXniBTZs28cUXX5CQkMBNN91U7XFr87ten6p7DwGGDBlSptZPP/20ymM2pPewuvM7+7yOHTvGnDlzMJlM3HLLLVUet6G8fzX5XHjiiSf49ttvWbRoEatXryY5OZm//OUvVR73XP52a82QOnXRRRcZEyZMsD+2WCxGZGSkMXXq1Arbjxgxwrj++uvL7IuNjTUeeOCBeq2zLqSlpRmAsXr16krbzJ071wgMDHRcUedhypQpRq9evWrc3pXfu9Mee+wxo3379obVaq3weVd6/wDjyy+/tD+2Wq1GeHi48eabb9r3ZWZmGl5eXsann35a6XFq+zfsSH8+x4ps2LDBAIxDhw5V2qa2v+uOUtH5jRkzxhg2bFitjtNQ38OavH/Dhg0zrrnmmirbNNT3zzDKfy5kZmYaHh4exqJFi+xtdu3aZQDGunXrKjzGuf7t1pZ6gOpQUVERGzduJC4uzr7Pzc2NuLg41q1bV+Fr1q1bV6Y9wODBgytt35BkZWUBEBwcXGW73Nxc2rRpQ3R0NMOGDWPHjh2OKO+c7N27l8jISNq1a8cdd9xBUlJSpW1d+b0D2+/rRx99xD333FPlor+u9P6dLTExkZSUlDLvUWBgILGxsZW+R+fyN9zQZGVlYTKZCAoKqrJdbX7XnW3VqlWEhobSuXNnxo8fT0ZGRqVtXfk9TE1NZcmSJdx7773Vtm2o79+fPxc2btxIcXFxmfejS5cutG7dutL341z+ds+FAlAdSk9Px2KxEBYWVmZ/WFgYKSkpFb4mJSWlVu0bCqvVyuOPP86ll17KBRdcUGm7zp07M2fOHL7++ms++ugjrFYrl1xyCUeOHHFgtTUTGxvLvHnzWLp0KTNmzCAxMZHLL7+cnJycCtu76nt32ldffUVmZiZ33313pW1c6f37s9PvQ23eo3P5G25ICgoKePbZZ7n99turXGSytr/rzjRkyBA+/PBD4uPjef3111m9ejVDhw7FYrFU2N6V38P58+fj7+9f7eWhhvr+VfS5kJKSgqenZ7lAXt3n4uk2NX3NudBq8HJOJkyYwPbt26u97jxgwAAGDBhgf3zJJZfQtWtX3n//fV555ZX6LrNWhg4dat/u2bMnsbGxtGnThs8++6xG/0fmambPns3QoUOJjIystI0rvX9NXXFxMSNGjMAwDGbMmFFlW1f6XR81apR9u0ePHvTs2ZP27duzatUqBg4c6MTK6t6cOXO44447qr3RoKG+fzX9XGgo1ANUh0JCQjCbzeVGt6emphIeHl7ha8LDw2vVviF4+OGH+d///sePP/5Iq1atavVaDw8P+vTpw759++qpuroTFBREp06dKq3VFd+70w4dOsSKFSu47777avU6V3r/Tr8PtXmPzuVvuCE4HX4OHTrE8uXLq+z9qUh1v+sNSbt27QgJCam0Vld9D3/++WcSEhJq/TcJDeP9q+xzITw8nKKiIjIzM8u0r+5z8XSbmr7mXCgA1SFPT0/69u1LfHy8fZ/VaiU+Pr7M/0WfbcCAAWXaAyxfvrzS9s5kGAYPP/wwX375JStXrqRt27a1PobFYmHbtm1ERETUQ4V1Kzc3l/3791daqyu9d382d+5cQkNDuf7662v1Old6/9q2bUt4eHiZ9yg7O5tff/210vfoXP6Gne10+Nm7dy8rVqygRYsWtT5Gdb/rDcmRI0fIyMiotFZXfA/B1iPbt29fevXqVevXOvP9q+5zoW/fvnh4eJR5PxISEkhKSqr0/TiXv91zLV7q0IIFCwwvLy9j3rx5xs6dO43777/fCAoKMlJSUgzDMIy77rrLeO655+zt16xZY7i7uxv/+te/jF27dhlTpkwxPDw8jG3btjnrFCo1fvx4IzAw0Fi1apVx7Ngx+1d+fr69zZ/P76WXXjJ++OEHY//+/cbGjRuNUaNGGd7e3saOHTuccQpVevLJJ41Vq1YZiYmJxpo1a4y4uDgjJCTESEtLMwzDtd+7s1ksFqN169bGs88+W+45V3v/cnJyjM2bNxubN282AGPatGnG5s2b7XdA/fOf/zSCgoKMr7/+2ti6dasxbNgwo23btsapU6fsx7jmmmuMd955x/64ur9hR6vqHIuKioybbrrJaNWqlbFly5Yyf5eFhYX2Y/z5HKv7XW8o55eTk2M89dRTxrp164zExERjxYoVxoUXXmh07NjRKCgoqPT8GtJ7WN3vqGEYRlZWluHr62vMmDGjwmM05PevJp8LDz74oNG6dWtj5cqVxu+//24MGDDAGDBgQJnjdO7c2fjiiy/sj2vyt3u+FIDqwTvvvGO0bt3a8PT0NC666CJj/fr19ueuvPJKY8yYMWXaf/bZZ0anTp0MT09Po3v37saSJUscXHHNABV+zZ07197mz+f3+OOP2/9bhIWFGdddd52xadMmxxdfAyNHjjQiIiIMT09PIyoqyhg5cqSxb98++/Ou/N6d7YcffjAAIyEhodxzrvb+/fjjjxX+Tp4+B6vVarzwwgtGWFiY4eXlZQwcOLDcebdp08aYMmVKmX1V/Q07WlXnmJiYWOnf5Y8//mg/xp/PsbrfdUeq6vzy8/ONa6+91mjZsqXh4eFhtGnTxhg3bly5INOQ38PqfkcNwzDef/99w8fHx8jMzKzwGA35/avJ58KpU6eMhx56yGjevLnh6+tr3HzzzcaxY8fKHefs19Tkb/d8mUp/sIiIiEiToTFAIiIi0uQoAImIiEiTowAkIiIiTY4CkIiIiDQ5CkAiIiLS5CgAiYiISJOjACQiIiJNjgKQiIiINDkKQCIiNWAymfjqq6+cXYaI1BEFIBFp8O6++25MJlO5ryFDhji7NBFxUe7OLkBEpCaGDBnC3Llzy+zz8vJyUjUi4urUAyQiLsHLy4vw8PAyX82bNwdsl6dmzJjB0KFD8fHxoV27dixevLjM67dt28Y111yDj48PLVq04P777yc3N7dMmzlz5tC9e3e8vLyIiIjg4YcfLvN8eno6N998M76+vnTs2JFvvvmmfk9aROqNApCINAovvPACt9xyC3/88Qd33HEHo0aNYteuXQDk5eUxePBgmjdvzm+//caiRYtYsWJFmYAzY8YMJkyYwP3338+2bdv45ptv6NChQ5mf8dJLLzFixAi2bt3Kddddxx133MGJEyccep4iUkfqdG15EZF6MGbMGMNsNht+fn5lvv7xj38YhmEYgPHggw+WeU1sbKwxfvx4wzAM4z//+Y/RvHlzIzc31/78kiVLDDc3NyMlJcUwDMOIjIw0/v73v1daA2A8//zz9se5ubkGYHz//fd1dp4i4jgaAyQiLuHqq69mxowZZfYFBwfbtwcMGFDmuQEDBrBlyxYAdu3aRa9evfDz87M/f+mll2K1WklISMBkMpGcnMzAgQOrrKFnz572bT8/PwICAkhLSzvXUxIRJ1IAEhGX4OfnV+6SVF3x8fGpUTsPD48yj00mE1artT5KEpF6pjFAItIorF+/vtzjrl27AtC1a1f++OMP8vLy7M+vWbMGNzc3OnfujL+/PzExMcTHxzu0ZhFxHvUAiYhLKCwsJCUlpcw+d3d3QkJCAFi0aBH9+vXjsssu4+OPP2bDhg3Mnj0bgDvuuIMpU6YwZswYXnzxRY4fP84jjzzCXXfdRVhYGAAvvvgiDz74IKGhoQwdOpScnBzWrFnDI4884tgTFRGHUAASEZewdOlSIiIiyuzr3Lkzu3fvBmx3aC1YsICHHnqIiIgIPv30U7p16waAr68vP/zwA4899hj9+/fH19eXW265hWnTptmPNWbMGAoKCvj3v//NU089RUhICLfeeqvjTlBEHMpkGIbh7CJERM6HyWTiyy+/ZPjw4c4uRURchMYAiYiISJOjACQiIiJNjsYAiYjL05V8Eakt9QCJiIhIk6MAJCIiIk2OApCIiIg0OQpAIiIi0uQoAImIiEiTowAkIiIiTY4CkIiIiDQ5CkAiIiLS5Px/A1rjjQaTUxwAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_acc(train_ACC22,test_ACC22)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b73e2dbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 利用torch.nn实现前馈神经网络-多分类任务\n",
    "from collections import OrderedDict\n",
    "from torch.nn import CrossEntropyLoss\n",
    "from torch.optim import SGD\n",
    "# 定义自己的前馈神经网络\n",
    "class MyNet23(nn.Module):\n",
    "    \"\"\"\n",
    "    参数：  num_input：输入每层神经元个数，为一个列表数据\n",
    "            num_hiddens：隐藏层神经元个数\n",
    "            num_outs： 输出层神经元个数\n",
    "            num_hiddenlayer : 隐藏层的个数\n",
    "    \"\"\"\n",
    "    def __init__(self,num_hiddenlayer=1, num_inputs=28*28,num_hiddens=[256],num_outs=10,act='relu'):\n",
    "        super(MyNet23, self).__init__()\n",
    "        # 设置隐藏层和输出层的节点数\n",
    "        self.num_inputs, self.num_hiddens, self.num_outputs = num_inputs,num_hiddens,num_outs # 十分类问题\n",
    "\n",
    "        # 定义模型结构\n",
    "        self.input_layer = nn.Flatten()\n",
    "        # 若只有一层隐藏层\n",
    "        if num_hiddenlayer ==1:\n",
    "            self.hidden_layers = nn.Linear(self.num_inputs,self.num_hiddens[-1])\n",
    "        else: # 若有多个隐藏层\n",
    "            self.hidden_layers = nn.Sequential()\n",
    "            self.hidden_layers.add_module(\"hidden_layer1\", nn.Linear(self.num_inputs,self.num_hiddens[0]))\n",
    "            for i in range(0,num_hiddenlayer-1):\n",
    "                name = str('hidden_layer'+str(i+2))\n",
    "                self.hidden_layers.add_module(name, nn.Linear(self.num_hiddens[i],self.num_hiddens[i+1]))\n",
    "        self.output_layer = nn.Linear(self.num_hiddens[-1], self.num_outputs)\n",
    "        # 指代需要使用什么样子的激活函数\n",
    "        if act == 'relu':\n",
    "            self.act = nn.ReLU()\n",
    "        elif act == 'sigmoid':\n",
    "            self.act = nn.Sigmoid()\n",
    "        elif act == 'tanh':\n",
    "            self.act = nn.Tanh()\n",
    "        elif act == 'elu':\n",
    "            self.act = nn.ELU()\n",
    "        print(f'你本次使用的激活函数为 {act}')\n",
    "\n",
    "    def logistic(self, x):  # 定义logistic函数\n",
    "        x = 1.0 / (1.0 + torch.exp(-x))\n",
    "        return x\n",
    "    # 定义前向传播\n",
    "    def forward(self, x):\n",
    "        x = self.input_layer(x)\n",
    "        x = self.act(self.hidden_layers(x))\n",
    "        x = self.output_layer(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "d0486d4e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "你本次使用的激活函数为 relu\n"
     ]
    }
   ],
   "source": [
    "model23 = MyNet23()  \n",
    "model23 = model23.to(device)\n",
    "\n",
    "# 将训练过程定义为一个函数，方便实验三和实验四调用\n",
    "def train_and_test(model=model23):\n",
    "    MyModel = model\n",
    "    optimizer = SGD(MyModel.parameters(), lr=0.01)  # 优化函数\n",
    "    epochs = 20  # 训练轮数\n",
    "    criterion = CrossEntropyLoss() # 损失函数\n",
    "    train_all_loss23 = []  # 记录训练集上得loss变化\n",
    "    test_all_loss23 = []  # 记录测试集上的loss变化\n",
    "    train_ACC23, test_ACC23 = [], []\n",
    "    begintime23 = time.time()\n",
    "    for epoch in range(epochs):\n",
    "        train_l, train_epoch_count, test_epoch_count = 0, 0, 0\n",
    "        for data, labels in traindataloader3:\n",
    "            data, labels = data.to(device), labels.to(device)\n",
    "            pred = MyModel(data)\n",
    "            train_each_loss = criterion(pred, labels.view(-1))  # 计算每次的损失值\n",
    "            optimizer.zero_grad()  # 梯度清零\n",
    "            train_each_loss.backward()  # 反向传播\n",
    "            optimizer.step()  # 梯度更新\n",
    "            train_l += train_each_loss.item()\n",
    "            train_epoch_count += (pred.argmax(dim=1)==labels).sum()\n",
    "        train_ACC23.append(train_epoch_count.cpu()/len(traindataset3))\n",
    "        train_all_loss23.append(train_l)  # 添加损失值到列表中\n",
    "        with torch.no_grad():\n",
    "            test_loss, test_epoch_count= 0, 0\n",
    "            for data, labels in testdataloader3:\n",
    "                data, labels = data.to(device), labels.to(device)\n",
    "                pred = MyModel(data)\n",
    "                test_each_loss = criterion(pred,labels)\n",
    "                test_loss += test_each_loss.item()\n",
    "                test_epoch_count += (pred.argmax(dim=1)==labels).sum()\n",
    "            test_all_loss23.append(test_loss)\n",
    "            test_ACC23.append(test_epoch_count.cpu()/len(testdataset3))\n",
    "            print('epoch: %d | train loss:%.2f | test loss:%.2f | train acc:%.3f | test acc:%.3f' % (epoch + 1, train_all_loss23[-1], test_all_loss23[-1],\n",
    "                                                                                                                     train_ACC23[-1],test_ACC23[-1]))\n",
    "    endtime23 = time.time()\n",
    "    print(\"torch.nn实现前馈网络-多分类任务 %d轮 总用时: %.2fs\" % (epochs, endtime23 - begintime23))\n",
    "    # 返回训练集和测试集上的 损失值 与 准确率\n",
    "    return train_all_loss23,test_all_loss23,train_ACC23,test_ACC23"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "8a4c53d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 1 | train loss:410.29 | test loss:51.34 | train acc:0.546 | test acc:0.635\n",
      "epoch: 2 | train loss:252.32 | test loss:37.92 | train acc:0.671 | test acc:0.674\n",
      "epoch: 3 | train loss:203.57 | test loss:32.88 | train acc:0.707 | test acc:0.710\n",
      "epoch: 4 | train loss:181.34 | test loss:30.15 | train acc:0.738 | test acc:0.733\n",
      "epoch: 5 | train loss:167.57 | test loss:28.22 | train acc:0.760 | test acc:0.750\n",
      "epoch: 6 | train loss:157.55 | test loss:26.63 | train acc:0.774 | test acc:0.768\n",
      "epoch: 7 | train loss:149.87 | test loss:25.59 | train acc:0.788 | test acc:0.778\n",
      "epoch: 8 | train loss:143.56 | test loss:24.66 | train acc:0.797 | test acc:0.784\n",
      "epoch: 9 | train loss:138.67 | test loss:23.82 | train acc:0.804 | test acc:0.794\n",
      "epoch: 10 | train loss:134.39 | test loss:23.30 | train acc:0.810 | test acc:0.799\n",
      "epoch: 11 | train loss:130.97 | test loss:22.79 | train acc:0.814 | test acc:0.805\n",
      "epoch: 12 | train loss:127.95 | test loss:22.29 | train acc:0.819 | test acc:0.809\n",
      "epoch: 13 | train loss:125.39 | test loss:22.09 | train acc:0.822 | test acc:0.807\n",
      "epoch: 14 | train loss:123.03 | test loss:21.58 | train acc:0.826 | test acc:0.814\n",
      "epoch: 15 | train loss:121.14 | test loss:21.40 | train acc:0.827 | test acc:0.817\n",
      "epoch: 16 | train loss:119.30 | test loss:21.04 | train acc:0.829 | test acc:0.817\n",
      "epoch: 17 | train loss:117.65 | test loss:20.77 | train acc:0.831 | test acc:0.820\n",
      "epoch: 18 | train loss:116.16 | test loss:20.54 | train acc:0.832 | test acc:0.820\n",
      "epoch: 19 | train loss:114.80 | test loss:20.44 | train acc:0.835 | test acc:0.820\n",
      "epoch: 20 | train loss:113.71 | test loss:20.26 | train acc:0.835 | test acc:0.819\n",
      "torch.nn实现前馈网络-多分类任务 20轮 总用时: 157.92s\n"
     ]
    }
   ],
   "source": [
    "train_all_loss23,test_all_loss23,train_ACC23,test_ACC23 = train_and_test(model=model23)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "47b54ecd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_loss(train_all_loss23,test_all_loss23)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "e9925908",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_acc(train_ACC23,test_ACC23)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ae8c206",
   "metadata": {},
   "source": [
    "## 使用至少三种不同的激活函数"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b38dff3e",
   "metadata": {},
   "source": [
    "### 使用tanh,sigmoid,elu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "def21666",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "你本次使用的激活函数为 tanh\n",
      "epoch: 1 | train loss:378.35 | test loss:47.35 | train acc:0.584 | test acc:0.655\n",
      "epoch: 2 | train loss:238.67 | test loss:36.56 | train acc:0.679 | test acc:0.684\n",
      "epoch: 3 | train loss:197.88 | test loss:32.15 | train acc:0.717 | test acc:0.717\n",
      "epoch: 4 | train loss:177.77 | test loss:29.52 | train acc:0.746 | test acc:0.741\n",
      "epoch: 5 | train loss:164.76 | test loss:27.69 | train acc:0.765 | test acc:0.758\n",
      "epoch: 6 | train loss:155.29 | test loss:26.30 | train acc:0.780 | test acc:0.773\n",
      "epoch: 7 | train loss:147.87 | test loss:25.27 | train acc:0.791 | test acc:0.780\n",
      "epoch: 8 | train loss:142.05 | test loss:24.35 | train acc:0.799 | test acc:0.788\n",
      "epoch: 9 | train loss:137.23 | test loss:23.67 | train acc:0.805 | test acc:0.793\n",
      "epoch: 10 | train loss:133.05 | test loss:23.06 | train acc:0.810 | test acc:0.798\n",
      "epoch: 11 | train loss:129.73 | test loss:22.54 | train acc:0.814 | test acc:0.803\n",
      "epoch: 12 | train loss:126.76 | test loss:22.14 | train acc:0.818 | test acc:0.808\n",
      "epoch: 13 | train loss:124.39 | test loss:21.78 | train acc:0.821 | test acc:0.810\n",
      "epoch: 14 | train loss:122.02 | test loss:21.40 | train acc:0.823 | test acc:0.814\n",
      "epoch: 15 | train loss:120.15 | test loss:21.12 | train acc:0.826 | test acc:0.815\n",
      "epoch: 16 | train loss:118.48 | test loss:20.93 | train acc:0.829 | test acc:0.814\n",
      "epoch: 17 | train loss:116.82 | test loss:20.67 | train acc:0.830 | test acc:0.818\n",
      "epoch: 18 | train loss:115.41 | test loss:20.43 | train acc:0.832 | test acc:0.821\n",
      "epoch: 19 | train loss:114.26 | test loss:20.31 | train acc:0.834 | test acc:0.820\n",
      "epoch: 20 | train loss:112.97 | test loss:20.08 | train acc:0.835 | test acc:0.822\n",
      "torch.nn实现前馈网络-多分类任务 20轮 总用时: 161.64s\n"
     ]
    }
   ],
   "source": [
    "model31 = MyNet23(act='tanh') \n",
    "model31 = model31.to(device) # 若有gpu则放在gpu上训练\n",
    "# 调用实验二中定义的训练函数\n",
    "train_all_loss31,test_all_loss31,train_ACC31,test_ACC31 = train_and_test(model=model31)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "b3debb9c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "你本次使用的激活函数为 sigmoid\n",
      "epoch: 1 | train loss:521.55 | test loss:85.14 | train acc:0.397 | test acc:0.463\n",
      "epoch: 2 | train loss:474.12 | test loss:75.91 | train acc:0.541 | test acc:0.535\n",
      "epoch: 3 | train loss:416.37 | test loss:66.11 | train acc:0.577 | test acc:0.594\n",
      "epoch: 4 | train loss:363.64 | test loss:58.22 | train acc:0.618 | test acc:0.637\n",
      "epoch: 5 | train loss:323.50 | test loss:52.40 | train acc:0.650 | test acc:0.652\n",
      "epoch: 6 | train loss:293.75 | test loss:48.04 | train acc:0.674 | test acc:0.676\n",
      "epoch: 7 | train loss:270.88 | test loss:44.62 | train acc:0.691 | test acc:0.690\n",
      "epoch: 8 | train loss:252.87 | test loss:41.91 | train acc:0.702 | test acc:0.695\n",
      "epoch: 9 | train loss:238.15 | test loss:39.66 | train acc:0.711 | test acc:0.700\n",
      "epoch: 10 | train loss:226.16 | test loss:37.77 | train acc:0.717 | test acc:0.715\n",
      "epoch: 11 | train loss:216.02 | test loss:36.20 | train acc:0.723 | test acc:0.720\n",
      "epoch: 12 | train loss:207.31 | test loss:34.87 | train acc:0.727 | test acc:0.725\n",
      "epoch: 13 | train loss:199.96 | test loss:33.70 | train acc:0.732 | test acc:0.726\n",
      "epoch: 14 | train loss:193.68 | test loss:32.71 | train acc:0.735 | test acc:0.730\n",
      "epoch: 15 | train loss:188.13 | test loss:31.84 | train acc:0.738 | test acc:0.734\n",
      "epoch: 16 | train loss:183.33 | test loss:31.10 | train acc:0.741 | test acc:0.733\n",
      "epoch: 17 | train loss:179.20 | test loss:30.44 | train acc:0.744 | test acc:0.740\n",
      "epoch: 18 | train loss:175.50 | test loss:29.85 | train acc:0.747 | test acc:0.744\n",
      "epoch: 19 | train loss:172.26 | test loss:29.32 | train acc:0.750 | test acc:0.744\n",
      "epoch: 20 | train loss:169.07 | test loss:28.85 | train acc:0.752 | test acc:0.747\n",
      "torch.nn实现前馈网络-多分类任务 20轮 总用时: 159.05s\n"
     ]
    }
   ],
   "source": [
    "model32 = MyNet23(act='sigmoid') \n",
    "model32 = model32.to(device) # 若有gpu则放在gpu上训练\n",
    "# 调用实验二中定义的训练函数\n",
    "train_all_loss32,test_all_loss32,train_ACC32,test_ACC32 = train_and_test(model=model32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "297c9dd6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "你本次使用的激活函数为 elu\n",
      "epoch: 1 | train loss:380.37 | test loss:46.68 | train acc:0.579 | test acc:0.661\n",
      "epoch: 2 | train loss:233.80 | test loss:35.77 | train acc:0.683 | test acc:0.691\n",
      "epoch: 3 | train loss:193.83 | test loss:31.53 | train acc:0.721 | test acc:0.723\n",
      "epoch: 4 | train loss:174.66 | test loss:28.99 | train acc:0.749 | test acc:0.743\n",
      "epoch: 5 | train loss:162.17 | test loss:27.23 | train acc:0.768 | test acc:0.762\n",
      "epoch: 6 | train loss:153.18 | test loss:25.94 | train acc:0.783 | test acc:0.774\n",
      "epoch: 7 | train loss:146.27 | test loss:24.94 | train acc:0.794 | test acc:0.786\n",
      "epoch: 8 | train loss:140.63 | test loss:24.17 | train acc:0.802 | test acc:0.791\n",
      "epoch: 9 | train loss:136.15 | test loss:23.48 | train acc:0.808 | test acc:0.796\n",
      "epoch: 10 | train loss:132.44 | test loss:22.91 | train acc:0.812 | test acc:0.800\n",
      "epoch: 11 | train loss:129.28 | test loss:22.45 | train acc:0.817 | test acc:0.807\n",
      "epoch: 12 | train loss:126.44 | test loss:22.08 | train acc:0.820 | test acc:0.808\n",
      "epoch: 13 | train loss:124.23 | test loss:21.73 | train acc:0.822 | test acc:0.810\n",
      "epoch: 14 | train loss:122.04 | test loss:21.37 | train acc:0.825 | test acc:0.815\n",
      "epoch: 15 | train loss:120.26 | test loss:21.17 | train acc:0.827 | test acc:0.813\n",
      "epoch: 16 | train loss:118.67 | test loss:20.89 | train acc:0.829 | test acc:0.817\n",
      "epoch: 17 | train loss:117.15 | test loss:20.76 | train acc:0.831 | test acc:0.817\n",
      "epoch: 18 | train loss:115.93 | test loss:20.57 | train acc:0.832 | test acc:0.821\n",
      "epoch: 19 | train loss:114.73 | test loss:20.33 | train acc:0.833 | test acc:0.821\n",
      "epoch: 20 | train loss:113.63 | test loss:20.16 | train acc:0.835 | test acc:0.823\n",
      "torch.nn实现前馈网络-多分类任务 20轮 总用时: 160.38s\n"
     ]
    }
   ],
   "source": [
    "model33 = MyNet23(act='elu') \n",
    "model33 = model33.to(device) # 若有gpu则放在gpu上训练\n",
    "# 调用实验二中定义的训练函数\n",
    "train_all_loss33,test_all_loss33,train_ACC33,test_ACC33 = train_and_test(model=model33)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "10460938",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(0, 20, 20)\n",
    "plt.subplot(1,4,1)\n",
    "plt.title(\"Train Loss\")\n",
    "plt.plot(x, train_all_loss23, label=\"ReLU\", linewidth=1.5)\n",
    "plt.plot(x, train_all_loss31, label=\"Tanh\", linewidth=1.5)\n",
    "plt.plot(x, train_all_loss32, label=\"Sigmoid\", linewidth=1.5)\n",
    "plt.plot(x, train_all_loss33, label=\"eLU\", linewidth=1.5)    \n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(1,4,2)\n",
    "plt.title(\"Test Loss\")\n",
    "plt.plot(x, test_all_loss23, label=\"ReLU\", linewidth=1.5)\n",
    "plt.plot(x, test_all_loss31, label=\"Tanh\", linewidth=1.5)\n",
    "plt.plot(x, test_all_loss32, label=\"Sigmoid\", linewidth=1.5)\n",
    "plt.plot(x, test_all_loss33, label=\"eLU\", linewidth=1.5)    \n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(1,4,3)\n",
    "plt.title(\"Train Acc\")\n",
    "plt.plot(x, train_ACC23, label=\"ReLU\", linewidth=1.5)\n",
    "plt.plot(x, train_ACC31, label=\"Tanh\", linewidth=1.5)\n",
    "plt.plot(x, train_ACC32, label=\"Sigmoid\", linewidth=1.5)\n",
    "plt.plot(x, train_ACC33, label=\"eLU\", linewidth=1.5)    \n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.ylabel(\"Acc\")\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(1,4,4)\n",
    "plt.title(\"Test Acc\")\n",
    "plt.plot(x, test_ACC23, label=\"ReLU\", linewidth=1.5)\n",
    "plt.plot(x, test_ACC31, label=\"Tanh\", linewidth=1.5)\n",
    "plt.plot(x, test_ACC32, label=\"Sigmoid\", linewidth=1.5)\n",
    "plt.plot(x, test_ACC33, label=\"eLU\", linewidth=1.5)    \n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.ylabel(\"Acc\")\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdbbb906",
   "metadata": {},
   "source": [
    "### 可以看到Sigmoid激活函数的损失大于其它激活函数，准确率不如其他函数，另外三种激活函数则在损失和激活率上大致相当，其中ReLU在训练轮数较小时不如其他激活函数，但随着轮数增加与其它激活函数相似"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5bca597",
   "metadata": {},
   "source": [
    "## 探究隐藏层层数与隐藏单元个数对实验的影响"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "617da97d",
   "metadata": {},
   "source": [
    "### 在任务2的多分类任务，我用了一个隐藏层，神经元个数为256；我将探究一个隐藏层，神经元个数为128、两个隐藏层，神经元个数为[512,256]和三个隐藏层，神经元个数为[512,256,128]三种情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "7ea3b4d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "你本次使用的激活函数为 relu\n",
      "epoch: 1 | train loss:411.81 | test loss:52.11 | train acc:0.552 | test acc:0.643\n",
      "epoch: 2 | train loss:256.11 | test loss:38.52 | train acc:0.670 | test acc:0.666\n",
      "epoch: 3 | train loss:206.19 | test loss:33.24 | train acc:0.704 | test acc:0.701\n",
      "epoch: 4 | train loss:183.44 | test loss:30.35 | train acc:0.733 | test acc:0.729\n",
      "epoch: 5 | train loss:169.42 | test loss:28.36 | train acc:0.756 | test acc:0.750\n",
      "epoch: 6 | train loss:159.15 | test loss:27.01 | train acc:0.771 | test acc:0.759\n",
      "epoch: 7 | train loss:151.34 | test loss:25.73 | train acc:0.784 | test acc:0.778\n",
      "epoch: 8 | train loss:145.01 | test loss:24.83 | train acc:0.794 | test acc:0.784\n",
      "epoch: 9 | train loss:139.88 | test loss:24.05 | train acc:0.802 | test acc:0.792\n",
      "epoch: 10 | train loss:135.54 | test loss:23.46 | train acc:0.807 | test acc:0.797\n",
      "epoch: 11 | train loss:132.01 | test loss:22.89 | train acc:0.813 | test acc:0.801\n",
      "epoch: 12 | train loss:128.93 | test loss:22.57 | train acc:0.817 | test acc:0.801\n",
      "epoch: 13 | train loss:126.34 | test loss:22.24 | train acc:0.820 | test acc:0.805\n",
      "epoch: 14 | train loss:123.99 | test loss:21.80 | train acc:0.822 | test acc:0.811\n",
      "epoch: 15 | train loss:122.01 | test loss:21.43 | train acc:0.826 | test acc:0.814\n",
      "epoch: 16 | train loss:120.16 | test loss:21.25 | train acc:0.826 | test acc:0.814\n",
      "epoch: 17 | train loss:118.67 | test loss:20.93 | train acc:0.828 | test acc:0.816\n",
      "epoch: 18 | train loss:117.09 | test loss:20.76 | train acc:0.831 | test acc:0.818\n",
      "epoch: 19 | train loss:115.79 | test loss:20.58 | train acc:0.832 | test acc:0.817\n",
      "epoch: 20 | train loss:114.59 | test loss:20.46 | train acc:0.834 | test acc:0.820\n",
      "torch.nn实现前馈网络-多分类任务 20轮 总用时: 176.89s\n"
     ]
    }
   ],
   "source": [
    "model41 = MyNet23(1,num_hiddens=[128]) \n",
    "model41 = model41.to(device) # 若有gpu则放在gpu上训练\n",
    "# 调用实验二中定义的训练函数\n",
    "train_all_loss41,test_all_loss41,train_ACC41,test_ACC41 = train_and_test(model=model41)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "10f9c1d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "你本次使用的激活函数为 relu\n",
      "epoch: 1 | train loss:452.05 | test loss:59.31 | train acc:0.506 | test acc:0.606\n",
      "epoch: 2 | train loss:280.98 | test loss:40.51 | train acc:0.645 | test acc:0.647\n",
      "epoch: 3 | train loss:213.59 | test loss:33.94 | train acc:0.682 | test acc:0.683\n",
      "epoch: 4 | train loss:186.18 | test loss:30.66 | train acc:0.714 | test acc:0.717\n",
      "epoch: 5 | train loss:170.35 | test loss:28.42 | train acc:0.744 | test acc:0.743\n",
      "epoch: 6 | train loss:159.00 | test loss:26.76 | train acc:0.765 | test acc:0.765\n",
      "epoch: 7 | train loss:150.01 | test loss:25.39 | train acc:0.781 | test acc:0.780\n",
      "epoch: 8 | train loss:142.81 | test loss:24.35 | train acc:0.794 | test acc:0.785\n",
      "epoch: 9 | train loss:136.63 | test loss:23.47 | train acc:0.803 | test acc:0.795\n",
      "epoch: 10 | train loss:131.70 | test loss:22.70 | train acc:0.809 | test acc:0.799\n",
      "epoch: 11 | train loss:127.71 | test loss:22.21 | train acc:0.815 | test acc:0.804\n",
      "epoch: 12 | train loss:124.28 | test loss:21.89 | train acc:0.819 | test acc:0.803\n",
      "epoch: 13 | train loss:121.37 | test loss:21.26 | train acc:0.822 | test acc:0.811\n",
      "epoch: 14 | train loss:118.97 | test loss:21.02 | train acc:0.826 | test acc:0.813\n",
      "epoch: 15 | train loss:116.93 | test loss:21.55 | train acc:0.828 | test acc:0.803\n",
      "epoch: 16 | train loss:115.40 | test loss:20.43 | train acc:0.829 | test acc:0.821\n",
      "epoch: 17 | train loss:113.79 | test loss:21.21 | train acc:0.832 | test acc:0.804\n",
      "epoch: 18 | train loss:112.30 | test loss:20.11 | train acc:0.834 | test acc:0.817\n",
      "epoch: 19 | train loss:111.00 | test loss:19.98 | train acc:0.836 | test acc:0.819\n",
      "epoch: 20 | train loss:109.77 | test loss:19.54 | train acc:0.837 | test acc:0.824\n",
      "torch.nn实现前馈网络-多分类任务 20轮 总用时: 185.48s\n"
     ]
    }
   ],
   "source": [
    "model42 = MyNet23(2,num_hiddens=[512,256]) \n",
    "model42 = model42.to(device) # 若有gpu则放在gpu上训练\n",
    "# 调用实验二中定义的训练函数\n",
    "train_all_loss42,test_all_loss42,train_ACC42,test_ACC42 = train_and_test(model=model42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "1cfafda5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "你本次使用的激活函数为 relu\n",
      "epoch: 1 | train loss:503.46 | test loss:74.61 | train acc:0.329 | test acc:0.427\n",
      "epoch: 2 | train loss:341.63 | test loss:46.33 | train acc:0.557 | test acc:0.592\n",
      "epoch: 3 | train loss:237.99 | test loss:36.87 | train acc:0.643 | test acc:0.655\n",
      "epoch: 4 | train loss:199.70 | test loss:32.64 | train acc:0.686 | test acc:0.680\n",
      "epoch: 5 | train loss:179.30 | test loss:29.85 | train acc:0.717 | test acc:0.722\n",
      "epoch: 6 | train loss:165.89 | test loss:28.35 | train acc:0.742 | test acc:0.734\n",
      "epoch: 7 | train loss:155.84 | test loss:26.51 | train acc:0.759 | test acc:0.758\n",
      "epoch: 8 | train loss:147.88 | test loss:25.30 | train acc:0.775 | test acc:0.769\n",
      "epoch: 9 | train loss:141.55 | test loss:24.62 | train acc:0.786 | test acc:0.778\n",
      "epoch: 10 | train loss:136.37 | test loss:24.30 | train acc:0.796 | test acc:0.780\n",
      "epoch: 11 | train loss:132.06 | test loss:23.20 | train acc:0.804 | test acc:0.792\n",
      "epoch: 12 | train loss:128.32 | test loss:22.69 | train acc:0.810 | test acc:0.796\n",
      "epoch: 13 | train loss:125.20 | test loss:22.60 | train acc:0.814 | test acc:0.795\n",
      "epoch: 14 | train loss:122.53 | test loss:21.73 | train acc:0.818 | test acc:0.805\n",
      "epoch: 15 | train loss:119.79 | test loss:21.42 | train acc:0.822 | test acc:0.808\n",
      "epoch: 16 | train loss:117.89 | test loss:21.08 | train acc:0.824 | test acc:0.810\n",
      "epoch: 17 | train loss:116.06 | test loss:22.21 | train acc:0.827 | test acc:0.790\n",
      "epoch: 18 | train loss:114.44 | test loss:20.35 | train acc:0.829 | test acc:0.816\n",
      "epoch: 19 | train loss:113.05 | test loss:20.26 | train acc:0.831 | test acc:0.817\n",
      "epoch: 20 | train loss:111.66 | test loss:20.23 | train acc:0.833 | test acc:0.820\n",
      "torch.nn实现前馈网络-多分类任务 20轮 总用时: 184.31s\n"
     ]
    }
   ],
   "source": [
    "model43 = MyNet23(3,num_hiddens=[512,256,128]) \n",
    "model43 = model43.to(device) # 若有gpu则放在gpu上训练\n",
    "# 调用实验二中定义的训练函数\n",
    "train_all_loss43,test_all_loss43,train_ACC43,test_ACC43 = train_and_test(model=model43)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "b73f0e7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(0, 20, 20)\n",
    "plt.subplot(1,4,1)\n",
    "plt.title(\"Train Loss\")\n",
    "plt.plot(x, train_all_loss23, label=\"[256]\", linewidth=1.5)\n",
    "plt.plot(x, train_all_loss41, label=\"[128]\", linewidth=1.5)\n",
    "plt.plot(x, train_all_loss42, label=\"[512,256]\", linewidth=1.5)\n",
    "plt.plot(x, train_all_loss43, label=\"[512,256,128]\", linewidth=1.5)    \n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(1,4,2)\n",
    "plt.title(\"Test Loss\")\n",
    "plt.plot(x, test_all_loss23, label=\"[256]\", linewidth=1.5)\n",
    "plt.plot(x, test_all_loss41, label=\"[128]\", linewidth=1.5)\n",
    "plt.plot(x, test_all_loss42, label=\"[512,256]\", linewidth=1.5)\n",
    "plt.plot(x, test_all_loss43, label=\"[512,256,128]\", linewidth=1.5)    \n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(1,4,3)\n",
    "plt.title(\"Train Acc\")\n",
    "plt.plot(x, train_ACC23, label=\"[256]\", linewidth=1.5)\n",
    "plt.plot(x, train_ACC41, label=\"[128]\", linewidth=1.5)\n",
    "plt.plot(x, train_ACC42, label=\"[512,256]\", linewidth=1.5)\n",
    "plt.plot(x, train_ACC43, label=\"[512,256,128]\", linewidth=1.5)    \n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.ylabel(\"Acc\")\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(1,4,4)\n",
    "plt.title(\"Test Acc\")\n",
    "plt.plot(x, test_ACC23, label=\"[256]\", linewidth=1.5)\n",
    "plt.plot(x, test_ACC41, label=\"[128]\", linewidth=1.5)\n",
    "plt.plot(x, test_ACC42, label=\"[512,256]\", linewidth=1.5)\n",
    "plt.plot(x, test_ACC43, label=\"[512,256,128]\", linewidth=1.5)    \n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.ylabel(\"Acc\")\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f999a60",
   "metadata": {},
   "source": [
    "### 可以看到随着隐藏层数量增加以及隐藏层内神经元数量的增加，损失增大且准确率下降，而且还出现了随着训练轮数波动的情况，可能是出现了过拟合的情况，隐藏层为一层的时候效果最好"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:.conda-pytorch] *",
   "language": "python",
   "name": "conda-env-.conda-pytorch-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
